Data processing devices, data processing units, methods and computer programs for processing telemetry data

ABSTRACT

A data processing device for processing telemetry data obtains sampled data based on output data from a plurality of sensors associated with a vehicle. The data processing device generates first and second sets of sampled values using the sampled data. The first set of sampled values are associated with a first sampling time and the second set of sampled values are associated with a second, subsequent sampling time. The data processing device derives a set of data elements, a data element being indicative of a measure of a change between a sampled value in the first set and a corresponding sampled value in the second set, a position of a given data element in the set of data elements having been determined based on at least one mapping rule. The data processing device encodes the set of data elements and outputs data comprising at least the encoded set of data elements for transmission to a remote data processing unit.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/126,939, filed Sep. 10, 2018, which is a continuation ofInternational Patent Application No. PCT/GB2017/050673, filed Mar. 13,2017, which claims priority to GB Application No. GB1604242.6, filedMar. 11, 2016, under 35 U.S.C. § 119(a). Each of the above-referencedpatent applications is incorporated by reference in its entirety.

BACKGROUND OF THE INVENTION Field of the Invention

The present invention relates to data processing devices, dataprocessing units, methods and computer programs for processing telemetrydata.

Description of the Related Technology

Aircraft have a large number of sensors that can be used to obtainmeasurement data relating to the aircraft and/or flight. Such sensorsmay measure, for example, temperature, humidity, air pressure, altitude,control positions, mechanical strain on hardware components of theaircraft etc. Some aircraft contain over ten thousand such sensors.

An on-board flight recorder, often known as a “black box”, records somesuch data and can be used to facilitate investigation of aviationaccidents and incidents. A flight recorder can, however, be difficult,or in some cases impossible, to locate following an accident orincident. Further, flight recorders typically only record a relativelysmall number of the different types of measurement data available. Forexample, older flight recorders may record only five different types ofmeasurement data. More recent flight recorders may record severalhundred different types of measurement data, but this is still much lessthan the overall amount of measurement data available. In such cases,the flight recorders are not recording the other, potentially useful,measurement data.

In some known systems, a flight recorder records measurement data duringa flight. The recorded data is downloaded from the aircraft followingarrival at the flight destination, compressed and archived, for exampleon a hard disk drive. The archived data can then be analyzed, forexample to assess degradation of aircraft parts, to predict when suchparts might need to be repaired or replaced etc.

In other known systems, measurement data is transmitted to the groundduring a flight. However, it may be impractical or impossible totransmit all of the measurement data available during the flight giventhe limited capacity of the communication channel to the ground and thelarge number of sensors.

SUMMARY

According to a first aspect of the present invention, there is provideda data processing device for processing telemetry data, the dataprocessing device being configured to:

obtain sampled data, the sampled data being based on output data from aplurality of sensors associated with a vehicle;

generate first and second sets of sampled values using the sampled data,the first set of sampled values being associated with a first samplingtime and the second set of sampled values being associated with asecond, subsequent sampling time;

derive a set of data elements, a data element being indicative of ameasure of a change between a sampled value in the first set of sampledvalues and a corresponding sampled value in the second set of sampledvalues, a position of a given data element in the set of data elementshaving been determined based on at least one mapping rule;encode the set of data elements; andoutput data comprising at least the encoded set of data elements fortransmission to a remote data processing unit.

According to a second aspect of the present invention, there is provideda data processing unit for processing telemetry data, the dataprocessing unit being configured to:

receive data comprising an encoded set of data elements from a remotedata processing device, a data element in the encoded set of dataelements being indicative of a measure of a change between a sampledvalue in a first set of sampled values and a corresponding sampled valuein a second set of sampled values, a position of a given data element inthe set of data elements having been determined based on at least onemapping rule, the first set of sampled values being associated with afirst sampling time and the second set of sampled values beingassociated with a second, subsequent sampling time, the first and secondsets of sampled values having been generated using sampled data, thesampled data being based on output data sampled from a plurality ofsensors associated with a vehicle;decode the encoded set of data elements; anduse at least the decoded set of data elements to recover the second setof sampled values.

According to a third aspect of the present invention, there is provideda method of processing telemetry data, the method comprising, at a dataprocessing device:

obtaining sampled data, the sampled data being based on output data froma plurality of sensors associated with a vehicle;

generating first and second sets of sampled values using the sampleddata, the first set of sampled values being associated with a firstsampling time and the second set of sampled values being associated witha second, subsequent sampling time;

deriving a set of data elements, a data element being indicative of ameasure of a change between a sampled value in the first set of sampledvalues and a corresponding sampled value in the second set of sampledvalues, a position of a given data element in the set of data elementshaving been determined based on at least one mapping rule;

encoding the set of data elements; and

outputting data comprising at least the encoded set of data elements fortransmission to a remote data processing unit.

According to a fourth aspect of the present invention, there is provideda method of processing telemetry data, the method comprising, at a dataprocessing unit:

receiving data comprising an encoded set of data elements from a remotedata processing device, a data element in the encoded set of dataelements being indicative of a measure of a change between a sampledvalue in a first set of sampled values and a corresponding sampled valuein a second set of sampled values, a position of a given data element inthe set of data elements having been determined based on at least onemapping rule, the first set of sampled values being associated with afirst sampling time and the second set of sampled values beingassociated with a second, subsequent sampling time, the first and secondsets of sampled values having been generated using sampled data, thesampled data being based on output data sampled from a plurality ofsensors associated with a vehicle;

decoding the encoded set of data elements; and

using at least the decoded set of data elements to recover the secondset of sampled values.

According to a fifth aspect of the present invention, there is provideda computer program comprising instructions which, when executed, cause adata processing device to perform a method comprising:

obtaining sampled data, the sampled data being based on output data froma plurality of sensors associated with a vehicle;

generating first and second sets of sampled values using the sampleddata, the first set of sampled values being associated with a firstsampling time and the second set of sampled values being associated witha second, subsequent sampling time;

deriving a set of data elements, a data element being indicative of ameasure of a change between a sampled value in the first set of sampledvalues and a corresponding sampled value in the second set of sampledvalues, a position of a given data element in the set of data elementshaving been determined based on at least one mapping rule;

encoding the set of data elements; and

outputting data comprising at least the encoded set of data elements fortransmission to a remote data processing unit.

According to a sixth aspect of the present invention, there is provideda computer program comprising instructions which, when executed, cause adata processing unit to perform a method comprising:

receiving data comprising an encoded set of data elements from a remotedata processing device, a data element in the encoded set of dataelements being indicative of a measure of a change between a sampledvalue in a first set of sampled values and a corresponding sampled valuein a second set of sampled values, a position of a given data element inthe set of data elements having been determined based on at least onemapping rule, the first set of sampled values being associated with afirst sampling time and the second set of sampled values beingassociated with a second, subsequent sampling time, the first and secondsets of sampled values having been generated using sampled data, thesampled data being based on output data sampled from a plurality ofsensors associated with a vehicle;

decoding the encoded set of data elements; and

using at least the decoded set of data elements to recover the secondset of sampled values.

According to a seventh aspect of the present invention, there isprovided a data processing device for processing telemetry data, thedata processing device being configured to:

obtain sampled data, the sampled data being based on output data from aplurality of sensors associated with a vehicle;

generate first and second sets of sampled values using the sampled data,the first set of sampled values being associated with a first samplingtime and the second set of sampled values being associated with asecond, subsequent sampling time;

derive a set of data elements, a data element being indicative of ameasure of a change between a sampled value in the first set of sampledvalues and a corresponding sampled value in the second set of sampledvalues;

encode the set of data elements; and

output data comprising at least the encoded set of data elements fortransmission to a remote data processing unit.

According to an eighth aspect of the present invention, there isprovided a data processing unit for processing telemetry data, the dataprocessing unit being configured to:

receive data comprising an encoded set of data elements from a remotedata processing device, a data element in the encoded set of dataelements being indicative of a measure of a change between a sampledvalue in a first set of sampled values and a corresponding sampled valuein a second set of sampled values, the first set of sampled values beingassociated with a first sampling time and the second set of sampledvalues being associated with a second, subsequent sampling time, thefirst and second sets of sampled values having been generated usingsampled data, the sampled data being based on output data sampled from aplurality of sensors associated with a vehicle;

decode the encoded set of data elements; and

use at least the decoded set of data elements to recover the second setof sampled values.

Further features and advantages will become apparent from the followingdescription of embodiments, given by way of example only, which is madewith reference to the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a schematic block diagram of an example of a dataprocessing system in accordance with an embodiment of the presentinvention;

FIG. 2 shows a schematic diagram of a series of graphs illustratingexamples of output data from a plurality of sensors associated with avehicle;

FIG. 3 shows a table comprising example data generated by a dataprocessing device in accordance with an embodiment of the presentinvention;

FIG. 4 shows a schematic block diagram of an example of a dataprocessing system in accordance with an embodiment of the presentinvention;

FIG. 5 shows schematically an illustration of an example of a method ofprocessing telemetry data in accordance with an embodiment of thepresent invention;

FIG. 6 shows schematically an illustration of an example of a method ofprocessing telemetry data in accordance with an embodiment of thepresent invention;

FIG. 7 shows schematically an illustration of an example of a method ofprocessing telemetry data in accordance with an embodiment of thepresent invention;

FIG. 8 shows schematically an illustration of an example of a method ofprocessing telemetry data in accordance with an embodiment of thepresent invention;

FIG. 9 shows a schematic block diagram of an example of a dataprocessing system in accordance with an embodiment of the presentinvention;

FIG. 10 shows a schematic block diagram of an example of a method ofprocessing telemetry data in accordance with an embodiment of thepresent invention;

FIG. 11 shows a schematic block diagram of an example of a method ofprocessing telemetry data in accordance with an embodiment of thepresent invention;

FIG. 12 shows a schematic block diagram of an example of a method ofprocessing telemetry data in accordance with an embodiment of thepresent invention;

FIG. 13 shows a schematic block diagram of an example of a method ofprocessing telemetry data in accordance with an embodiment of thepresent invention;

FIG. 14 shows schematically a series of graphs illustrating examples ofoutput data from a plurality of sensors associated with a vehicle;

FIG. 15 shows schematically a series of graphs illustrating examples ofoutput data from a plurality of sensors associated with a vehicle;

FIG. 16 shows schematically a series of graphs illustrating examples ofoutput data from a plurality of sensors associated with a vehicle;

FIG. 17 shows a schematic block diagram of an example of a dataprocessing system in accordance with an embodiment of the presentinvention;

FIG. 18 shows a schematic block diagram of an example of a dataprocessing system in accordance with an embodiment of the presentinvention; and

FIG. 19 shows a schematic block diagram of an example of an apparatus inaccordance with an embodiment of the present invention.

DETAILED DESCRIPTION OF CERTAIN INVENTIVE EMBODIMENTS

Referring to FIG. 1, there is shown a schematic block diagram of anexample of a data processing system 100.

The data processing system 100 includes a data processing device 101.The data processing device 101 is configured to process telemetry data.Telemetry concerns collecting one or more measurements at a first siteand making the one or more measurements available at a second, remotesite.

The data processing device 101 may comprise one or more hardware and/orone or more software components. In some examples, the data processingdevice 101 is a dedicated device whose sole function is to processtelemetry data in the manner described herein.

The data processing device 101 is associated with a vehicle 102. In thisexample, the data processing device 101 is provided in the vehicle 102.For example, the data processing device 101 may be mounted inside thevehicle. Examples of vehicle include, but are not limited to, aircraft,spacecraft, road vehicles, boats etc.

A plurality of sensors 103, 104, 105, 106 is associated with the vehicle102. The sensors 103, 104, 105, 106 may be associated with the vehicle102 by being provided in and/or on the vehicle 102.

In this specific example, the data processing system 100 includes foursensors 103, 104, 105, 106, denoted S₁, S₂, S₃, S₄ respectively. It willbe appreciated that a different number of sensors 103, 104, 105, 106could however be used. In reality, significantly more than four sensors103, 104, 105, 106 may be used. Tens of thousands of sensors, or evenmore, could be used in an aircraft for example. In other examples, fewerthan four sensors 103, 104, 105, 106 could be used.

A sensor 103, 104, 105, 106 measures at least one physical property andproduces corresponding output data. Examples of such physical propertyinclude, but are not limited to, temperature, pressure, speed,direction, altitude, mechanical strain, operating position of a button,operating position of a switch etc. Such a physical property may relateto the vehicle 102 itself, for example in the case of an operatingposition of a switch on the vehicle 102. Such a physical property mayrelate to an environment in which the vehicle 102 is located, forexample, in the case of a temperature outside the vehicle 102.

Output data from a sensor 103, 104, 105, 106 may take different forms.The form of the output data may depend on the nature of the sensor 103,104, 105, 106. The form of the output data may depend on the nature ofthe at least one physical property the sensor 103, 104, 105, 106 ismeasuring. In some examples, the sensor 103, 104, 105, 106 outputs datain an analogue form. In some examples, the sensor 103, 104, 105, 106outputs data in a digital form. In some examples, the output dataincludes further data in addition to data corresponding to the at leastone measured physical property. An example of such further data includesdata identifying the sensor 103, 104, 105, 106.

In some examples, a sensor 103, 104, 105, 106 is configured to outputdata continuously. In other examples, a sensor 103, 104, 105, 106 isconfigured to output data intermittently. For example, a sensor 103,104, 105, 106 may be configured to output data periodically.

The output data from the sensors 103, 104, 105, 106 is sampled at one ormore sampling rates. For example, the sensors 103, 104, 105, 106 may besynchronized so that they are all sampled at the same sampling rate.Alternatively, at least some of the sensors 103, 104, 105, 106 may besampled at different sampling rates.

The data processing device 101 is configured to obtain sampled data. Thesampled data is based on the output data from the sensors 103, 104, 105,106.

In some examples, the data processing device 101 obtains the sampleddata by directly sampling the output data of the sensors 103, 104, 105,106 at various different sampling times. In some examples, the dataprocessing device 101 obtains the sampled data by receiving the sampleddata from one or more entities intermediate the data processing device101 and the sensors 103, 104, 105, 106.

In this example, the data processing system 100 comprises a dataacquisition unit 107. In this example, the data acquisition unit 107provides the functionality of the one or more intermediate entitiesreferred to above. In particular, in this example, the data acquisitionunit 107 directly samples the output data from the sensors 103, 104,105, 106 and outputs sampled data based on such sampling to the dataprocessing device 101. The data acquisition unit 107 may comprise one ormore hardware and/or one or more software components configured toprovide the sampling functionality.

In this example, the data acquisition unit 107 is separate from the dataprocessing device 101. In this example, the data acquisition unit 107samples the output data from the sensors 103, 104, 105, 106 via arespective connection 108, 109, 110, 111 with each of the sensors 103,104, 105, 106. The connections 108, 109, 110, 111 may be physical orlogical connections. In this example, the data acquisition unit 107outputs the sampled data to the data processing device 101 via a singleconnection 112. In this example, the data processing device 101therefore has a single connection 112 to the data acquisition unit 107and the data acquisition unit 107 has multiple connections 108, 109,110, 111 to the sensors 103, 104, 105, 106. In such an example, the dataprocessing device 101 is indirectly connected to some or all the sensors103, 104, 105, 106.

In another example, the data processing device 101 comprises the dataacquisition unit 107 and the associated sampling functionality. In suchan example, the data processing device 101 samples the output data fromthe plurality of sensors 103, 104, 105, 106 directly by receiving theoutput data from the sensors 103, 104, 105, 106 and taking samples ofthe output data at different sampling times. In such an example, thedata processing device 101 is directly connected to some or all of thesensors 103, 104, 105, 106.

In some examples, the vehicle 102 is an aircraft. In some examples, thedata processing device 101 is compatible with existing hardware and/orsoftware on the aircraft. In some examples, the data processing device101 replaces one or more existing hardware and/or software components onthe aircraft to provide the functionality described herein. In someexamples, the data processing device 101 cooperates with existinghardware and/or software to provide the functionality described herein.

In some such examples, the data acquisition unit 107 comprises one ormore flight-data acquisition units (FDAUs).

An FDAU receives output data from the sensors 103, 104, 105, 106. TheFDAU may receive the output data from the sensors 103, 104, 105, 106 ina specific data format. In some examples, the specific data formatcomplies with one or more standards. Examples of such standardizedcommunication protocols, developed by Aeronautical Radio, Incorporated(ARINC), are ARINC 429 and ARINC 717.

The FDAU outputs sampled data based on the output data from the sensors103, 104, 105, 106 to one or more entities. The data output by the FDAUmay be in a specific data format. In some examples, the specific dataformat complies with one or more standards. Examples of such standards,developed by ARINC, are ARINC 573, ARINC 717 and ARINC 747.

An example of an entity to which the FDAU may output sampled data is aflight data recorder (FDR). An FDR records data relating to a flight. AnFDR is designed to survive an accident involving the aircraft. In somesuch examples, the data processing device 101 performs the function ofan FDR. The data processing device 101 may operate in association withan existing FDR or may replace an existing, for example legacy, FDR.

Another example of an entity to which the FDAU may output sampled datais a quick access recorder (QAR). A QAR is a flight data recorderdesigned to provide quick and easy access to raw data relating to aflight. A QAR may record more types of data than an FDR. A QAR may beable to sample data from the FDAU at a higher rate than that achievableby an FDR. In some such examples, the data processing device 101performs the function of a QAR. The data processing device 101 mayoperate in association with an existing QAR or may replace an existing,for example legacy, QAR.

Where the data processing device 101 is used in association with anaircraft, the data processing device 101 may therefore be considered tobe a virtual flight recorder (or “virtual black box”).

The data processing device 101 is configured to generate first andsecond sets of sampled values using the sampled data. The first set ofsampled values is associated with a first sampling time. The second setof sampled values is associated with a second, subsequent sampling time.

Quantization involves constraining a continuous set of values to arelatively small set of discrete values. For example, suppose the outputdata of a given sensor varies continuously between “0” and “10”inclusive and that the output data is quantized by rounding it to thenearest integer. An unquantized output value of “4.7” would thereforecorrespond to a quantized value of “5”. Quantization may reduce theamount of telemetry data that needs to be transmitted. For example, anumber between “0” and “10” inclusive can be represented using only fourbits, whereas representing non-quantized values may require more thanfour bits. However, quantization comes at the cost of decreasedgranularity, detail and accuracy of the output data. In scenarios inwhich granularity, detail and accuracy are important, it may bepreferable not to use quantization and to report some or all of theactual, unquantized measured values instead. This may be important, forexample, where the telemetry data relates to data obtained from aircraftsensors. As such, in some examples, some or all of the sampled values inthe first set and/or second set of sampled values are unquantizedversions of values obtained from the output data of some or all of thesensors 103, 104, 105, 106. In some examples, however, some or all ofthe sampled values in the first set and/or second set of sampled valuesare quantized versions of values obtained from at least some of thesensors 103, 104, 105, 106. Quantization may in some cases bebeneficial, for example where granularity, detail and accuracy are lessimportant than the amount of data to be transmitted.

The data processing device 101 is configured to derive a set of dataelements. A data element is indicative of a measure of a change betweena sampled value in the first set of sampled values, associated with thefirst sampling time, and a corresponding sampled value in the second setof sampled values associated with the second sampling time. In thisexample, “corresponding” refers to the sampled value in the first set ofsampled values and the second set of sampled values having been obtainedfrom the same sensor as each other.

In some examples, the measure of the change is a difference between thesampled value in the first set of sampled values and the correspondingsampled value in the second set of sampled values. In some examples, thedifference is calculated by subtracting the sampled value in the firstset of sampled values from the corresponding sampled value in the secondset of sampled values. In some examples, the difference is calculated bysubtracting the sampled value in the second set of sampled values fromthe corresponding sampled value in the first set of sampled values. Inother examples, a different measure of the change may be used. Forexample, the measure of the change may be a ratio of the sampled valuein the first set relative to the sampled value in the second set.

The data processing device 101 is configured to encode the set of dataelements. Encoding relates to converting data from one form into anotherform. In this example, the data processing device 101 is configured toconvert the set of data elements from one form into another form.

The data processing device 101 is configured to output the encoded setof data elements for transmission to a remote data processing unit 113in the data processing system 100. In some examples, the data processingdevice 101 outputs the encoded set of data elements to at least oneother entity in the data processing system 100 and the at least oneother entity transmits the encoded set of data elements to the remotedata processing unit 113. In other examples, the data processing device101 has the capability to transmit the encoded set of data elements tothe remote data processing unit 113 itself. In some examples, the dataprocessing device 101 transmits the encoded set of data elements to theremote data processing unit 113 itself and also outputs the encoded setof data elements to at least one other entity in the data processingsystem 100 so that the at least one other entity can also transmit theencoded set of data elements to the remote data processing unit 113.

The encoded set of data elements is transmitted to the remote dataprocessing unit 113 over one or more communication channels 114established via one or more data communications networks 115. In someexamples, the transmission of the encoded set of data elements includeswireless transmission of the encoded set of data elements to the remotedata processing unit 113 via a wireless data communications network. Insome such examples, the data communications network 115 is a satellitenetwork.

In this example, the data processing system 100 allows telemetry data tobe transmitted substantially in real-time (or “live”). The term“substantially” in relation to real-time transmission of telemetry datais used herein as there are inevitably delays in obtaining, processingand transmitting the telemetry data from the data processing device 101to the remote data processing unit 113.

In the case of the vehicle 102 being an aircraft, the data processingdevice 101 may be configured to transmit telemetry data “in-flight”and/or “in-journey”. The term “in-flight” is used herein to mean thepart of the journey in which the aircraft is in the air. The term“in-journey” is used herein to include the part of the journey in whichthe aircraft is in the air and also one or more other parts of thejourney, for example fueling and/or taxiing.

The telemetry data transmitted from the data processing device 101 tothe remote data processing unit 113 may be used by one or moreinterested parties. Examples of such interested parties include, but arenot limited to, a manufacturer of the vehicle 102 and a service thatruns or manages the vehicle 102. Such data may be used, for example, forfailure detection and/or prediction, live diagnostics, metrologicalpurposes and the like.

In some examples, the vehicle 102 is an aircraft. In such examples, thetelemetry data may be used for flight operations quality assurance(FOQA), flight data monitoring (FDM) or flight data analysis purposes.Analysis of the telemetry data may help to improve flight safety and/oroperational efficiency, in particular where the telemetry data istransmitted substantially in real-time.

The communication channel 114 between the data processing device 101 andthe remote data processing unit 113 may have a relatively low bandwidthcompared to the amount of raw measurement data it would be desirable totransmit to the remote data processing unit 113. The communicationchannel 114 may additionally or alternatively have a high usage costsuch that transmitting all of the raw measurement data would beparticularly, and potentially prohibitively, expensive. The term “rawmeasurement data” is used herein to mean the measurement data availableto the data processing device 101 relating to the output data of thesensors 103, 104, 105, 106. Raw measurement data may include sampledvalues, and other related data. An example of such other related data issensor identification data.

Taking the example of the vehicle 102 being an aircraft, the measurementdata for a single sensor may, for example, comprise 32 bits. Themeasurement data may comprise a first, static portion, a middle, dynamicpayload portion, and a final, static portion. The initial, staticportion may for example comprise sensor identification data. The middle,dynamic portion may comprise a sampled value. Assuming the aircraft has18,000 sensors and that each sensor is sampled at a sampling rate of 18Hz, the required data rate for transmitting all of the raw measurementdata would be 32*18,000*18≈10 Mbit/s, which is significantly higher thanthe 64 kbit/s capacity of a satellite connection over the poles.

Encoding of the set of data elements reduces the amount of data requiredto transmit the set of data elements to the remote data processing unit113. The extent of encoding required may be determined based on one ormore factors. Some or all of the one or more factors may be associatedwith one or more characteristics of the communication channel 114between the data processing device 101 and the remote data processingunit 113. For example, the capacity of the communication channel 114 mayimpose restrictions or constraints on the extent of encoding required tobe able to communicate the telemetry data via the communication channel114. Another factor may be the cost of sending data via thecommunication channel 114. The cost of sending data via thecommunication channel 114 may depend, for example, on the nature of thecommunication channel 114. The extent of encoding required mayadditionally or alternatively be determined based on hardware and/orsoftware constraints of one or more entities in the data processingsystem 100. For example, encoding and decoding capabilities in the dataprocessing system 100 may affect the extent to which encoding is usedand/or the type of encoding that is used.

In this example, the data processing device 101 is configured to encryptthe first set of sampled values prior to outputting the first set ofsampled values in an encrypted form for transmission to the remote dataprocessing unit 113.

In this example, the data processing device 101 is configured to outputthe set of data elements in plaintext, unencrypted form for transmissionto the remote data processing unit 113.

If a third party were to obtain the plaintext, unencrypted set of dataelements, they could potentially infer the extent to which output datafrom a sensor 103, 104, 105, 106 has changed since a previous samplingtime. However, such information may be less sensitive than knowing theabsolute values of the output data from sensors 103, 104, 105, 106,which could be inferred from the first set of sampled values. As such,the first set of sampled values is, in this example, encrypted prior totransmission. In this example, a data element in the set of dataelements is indicative of a measure of a change between an unencryptedsampled value in the first set of sampled values and a correspondingsampled value in the second set of sampled values.

In some examples, the data processing device 101 is configured to encodethe set of data elements using a first codec and to encode the first setof sampled values using a second, different codec.

In some examples, the data processing device 101 is configured to encodethe set of data elements and/or the first set of sampled values based onat least one characteristic of the set of data elements and/or the firstset of sampled values. An example of such a characteristic is a mannerin which the set of data elements and/or the first set of sampled valuesis arranged. For example, the set of data elements and/or the first setof sampled values may be encoded using a specific encoding technique,for example an image coding technique, if they are arranged in the formof an array. Further, different types of array may be encodeddifferently. Another example of such a characteristic is a type of dataincluded in the set of data elements and/or the first set of sampledvalues. For example, numerical data may be encoded differently fromother types of data. Another example of such a characteristic is avariation of data included in the set of data elements and/or the firstset of sampled values. For example, data that varies widely across theset of data elements and/or the first set of sampled values may beencoded differently from data that is less varied.

In some examples, the data processing device 101 is configured to encodethe set of data elements and/or the first set of sampled values usingone or more image encoding techniques. In some examples describedherein, the set of data elements and/or the first set of sampled valuesare arranged in the form of an array of numerical values, which may makethem suited to being encoded using the one or more image encodingtechniques.

In some examples, the data processing device 101 is configured to encodethe set of data elements and/or the first set of sampled values usingone or more video encoding techniques. In some examples describedherein, the set of data elements and/or the first set of sampled valuesare arranged in the form of an array of numerical values, and there is atemporal correlation between the set of data elements and/or the firstset of sampled values, which may make them suited to being encoded usingthe one or more video encoding techniques.

In some examples, the data processing device 101 is configured to encodethe set of data elements and/or the first set of sampled values usingone or more lossless encoding techniques. An example of a losslessencoding technique is Run-Length Encoding (RLE). Using a losslessencoding technique allows all of the information being encoded to berecovered by a decoder. This may be beneficial where the accuracy andcompleteness of the information is important. However, informationencoded using a lossless coding technique may require more data thanusing a lossy encoding technique.

In some examples, the data processing device 101 is configured to encodethe set of data elements and/or the first set of sampled values usingone or more entropy encoding techniques. Entropy encoding techniques area form of lossless encoding technique. Examples of entropy codingtechniques include, but are not limited to, Hufmann coding, arithmeticcoding and range encoding. The reader is referred to WO-A2-2013/011495,which describes various examples of entropy encoding techniques. Theentire contents of WO-A2-2013/011495 are hereby incorporated herein byreference.

In some examples, the data processing device 101 is configured to encodethe set of data elements and/or the first set of sampled values using alossy encoding technique. As indicated above, a lossy encoding techniquemay result in less data being used than with a lossless encodingtechnique but this comes at the cost of reduced accuracy andcompleteness of the information recoverable by the decoder.

In some examples, the data processing device 101 is configured not toencode the second set of sampled values. In some examples, the dataprocessing device 101 is configured not to output the second set ofsampled values for transmission to the remote data processing unit 113.In some examples described herein, and in contrast to existing image orvideo encoding techniques, the data processing device 101 encodes thefirst set of sampled values and the set of data elements and transmitssuch encoded data to the remote data processing unit 113. The remotedata processing unit 113 can then recover or reconstruct the second setof sampled values using the first set of sampled values and the set ofdata elements without the data processing device 101 having to encode oroutput for transmission the second set of sampled values. Existing imageor video encoding techniques may for example encode both the first andsecond sets of sampled values and not create such a set of data elementsindicative of measures of changes between corresponding sampled valuesin the first and second sets of sampled values. As will be described inmore detail below, encoding and outputting for transmission the firstset of sampled values and the set of data elements but not encoding oroutputting for transmission the second set of sampled values may reducethe amount of data to be transmitted to the remote data processing unit113 while still enabling the remote data processing unit 113 to recoverthe second set of sampled values.

In some examples, the data processing device 101 is configured not toencode the first set of sampled values. In some examples, the dataprocessing device 101 is configured not to output the first set ofsampled values for transmission to the remote data processing unit 113.In such examples, the data processing device 101 and the remote dataprocessing unit 113 may have a common set of reference values and thedata processing device 101 may output for transmission to the remotedata processing unit 113 an initial set of data elements indicative of ameasure of a change between the set of reference values and the firstset of sampled values. The remote data processing unit 113 may then usethe initial set of data elements to obtain the first set of sampledvalues from the common set of reference values.

The techniques described herein are particularly, but not exclusively,efficient where the output data from a relatively large number of thesensors 103, 104, 105, 106 changes relatively slowly and/or where theoutput data from a relatively low number of the sensors 103, 104, 105,106 changes relatively quickly.

Referring to FIG. 2, there is shown schematically a series of graphsillustrating examples of output data from a plurality of sensors S₁, S₂,S₃, S₄ associated with a vehicle. In this example, output data from foursensors S₁, S₂, S₃, S₄ is illustrated.

In this example, the output of an i^(th) sensor, S₁, is denoted O(S₁).For example, the output of sensor S₁ is denoted O(S₁).

Output data from the first sensor S₁ is shown in a first graph 200. Thefirst graph 200 indicates how the output, O(S₁), of the first sensor S₁varies over time, t. In this example, the output, O(S₁), of the firstsensor S₁ remains at a constant value of “10” during the time periodshown on the first graph 200. In this example, the output, O(S₁), of thefirst sensor S₁ at a first sampling time, t₁, is “10” and the output,O(S₁), of the first sensor S₁ at a second sampling time, t₂, is also“10”. As such, the difference between the output, O(S₁), of the firstsensor S₁ at the first sampling time, t₁, and the output, O(S₁), of thefirst sensor S₁ at the second sampling time, t₂, is “0”.

Output data from the second sensor S₂ is shown in a corresponding secondgraph 201. The second graph 201 indicates how the output, O(S₂), of thesecond sensor S₂ varies over time, t. In this example, the output,O(S₂), of the second sensor S₂ remains at a constant value of “−2”during the time period shown on the second graph 201. In this example,the output, O(S₂), of the second sensor S₂ at the first sampling time,t₁, is “−2” and the output, O(S₂), of the second sensor S₂ at the secondsampling time, t₂, is also “−2”. As such, the difference between theoutput, O(S₂), of the second sensor S₂ at the first sampling time, t₁,and the output, O(S₂), of the second sensor S₂ at the second samplingtime, t₂, is “0”.

Output data from the third sensor S₃ is shown in a corresponding thirdgraph 202. The third graph 202 indicates how the output, O(S₃), of thethird sensor S₃ varies over time, t. In this example, the output, O(S₃),of the third sensor S₃ increases linearly over time during the timeperiod shown on the third graph 202. In this example, the output, O(S₃),of the third sensor S₃ at the first sampling time, t₁, is “1” and theoutput, O(S₃), of the third sensor S₃ at the second sampling time, t₂,increases to “2”. As such, the difference between the output, O(S₃), ofthe third sensor S₃ at the first sampling time, t₁, and the output,O(S₃), of the third sensor S₃ at the second sampling time, t₂, is “1”.

Output data from the fourth sensor S₄ is shown in a corresponding fourthgraph 203. The fourth graph 203 indicates how the output, O(S₄), of thefourth sensor S₄ varies over time, t. In this example, the output,O(S₄), of the fourth sensor S₄ remains at a constant value of “22”during the time period shown on the fourth graph 203. In this example,the output, O(S₄), of the fourth sensor S₄ at the first sampling time,t₁, is “22” and the output, O(S₄), of the fourth sensor S₄ at the secondsampling time, t₂, is also “22”. As such, the difference between theoutput, O(S₄), of the fourth sensor S₄ at the first sampling time, t₁,and the output, O(S₄), of the fourth sensor S₄ at the second samplingtime, t₂, is “0”.

Although the output data from the plurality of sensors S₁, S₂, S₃, S₄ isshown in the form of straight lines, it will be appreciated that inreality the output data from the plurality of sensors S₁, S₂, S₃, S₄ mayfluctuate slightly and deviate slightly from a perfect straight linewhile still remaining substantially linear. Furthermore, while theoutput data has been shown using straight lines for ease of explanation,it will be appreciated that, in reality, the output data may take manydifferent forms depending, for example, on the nature of the associatedsensor and/or the physical quantity being monitored.

Referring to FIG. 3, there is shown a table 300 comprising example datagenerated by a data processing device.

It will be appreciated that the data processing device may not storemeasurement data in the form of the table 300. The table 300 containsthe sampled values obtained from the output, O(S₁), O(S₂), O(S₃), O(S₄),of the four sensors S₁, S₂, S₃, S₄ described above with reference toFIG. 2. The table 300 therefore includes sampled values obtained fromfour sensors, S₁, S₂, S₃, S₄ at two different sampling times andcorresponding data element values.

A first sampled value of “10” is obtained from the first sensor S₁ atthe first sampling time, t₁, and a second sampled value of “10” isobtained from the first sensor S₁ at the second sampling time, t₂. Thedifference between the second sampled value and the first sampled valueobtained from the first sensor S₁ is therefore “0”. In this example, thedifference is calculated by subtracting the first sampled value of “10”from the second sampled value of “10”.

A first sampled value of “−2” is obtained from the second sensor S₂ atthe first sampling time, t₁, and a second sampled value of “−2” isobtained from the second sensor S₂ at the second sampling time, t₂. Thedifference between the second sampled value and the first sampled valueobtained from the second sensor S₂ is therefore “0”. In this example,the difference is calculated by subtracting the first sampled value of“−2” from the second sampled value of “−2”.

A first sampled value of “1” is obtained from the third sensor S₃ at thefirst sampling time, t₁, and a second sampled value of “2” is obtainedfrom the third sensor S₃ at the second sampling time, t₂. The differencebetween the second sampled value and the first sampled value obtainedfrom the third sensor S₃ is therefore “1”. In this example, thedifference is calculated by subtracting the first sampled value of “1”from the second sampled value of “2”.

A first sampled value of “22” is obtained from the fourth sensor S₄ atthe first sampling time, t₁, and a second sampled value of “22” isobtained from the fourth sensor S₄ at the second sampling time, t₂. Thedifference between the second sampled value and the first sampled valueobtained from the fourth sensor S₄ is therefore “0”. In this example,the difference is calculated by subtracting the first sampled value of“22” from the second sampled value of “22”.

Referring to FIG. 4, there is shown a schematic block diagram of anexample of a data processing system 400.

The data processing system 400 includes a data processing device 401.The data processing device 401 may have some or all of the samefunctionality as the data processing device 101 described above withreference to FIG. 1. In this example, the data processing device 401receives sampled values from a data acquisition unit (not shown). Thedata acquisition unit may have some or all of the same functionality asthe data acquisition unit 107 described above with reference to FIG. 1.As described above, the data acquisition unit may be comprised in thedata processing device 401 or may be separate from the data processingdevice 401.

In this example, the data processing device 401 includes a mapper 402.The mapper 402 may comprise one or more hardware and/or one or moresoftware components configure to provide mapping functionality. In someexamples, the mapper 402 is a logical component of the data processingdevice 401.

In this example, the mapper 402 receives input data from the dataacquisition unit (not shown) and maps the input data to output data.Such mapping may involve preserving and/or changing the order of atleast some of the output data in relation to the order of the input dataas will be described in detail below.

In this example, a sampled value obtained from an i^(th) sensor, S₁, isdenoted SV_(i). For example, a sampled value obtained from a firstsensor, S₁, is denoted SV₁.

In this example, an input set of sampled values 403 includes a firstsampled value SV₁ obtained from the first sensor S₁. The input set ofsampled values 403 further includes a second sampled value SV₂ obtainedfrom the second sensor S₂. The input set of sampled values 403 furtherincludes a third sampled value SV₃ obtained from the third sensor S₃.The input set of sampled values 403 further includes a fourth sampledvalue SV₄ obtained from the fourth sensor S₄.

In this example, an output set of sampled values 404 includes the firstsampled value SV₁. The output set of sampled values 404 further includesthe second sampled value SV₂. The output set of sampled values 404further includes the third sampled value SV₃. The output set of sampledvalues 404 further includes the fourth sampled value SV₄.

In this example, the input set of sampled values 403 and the output setof sampled values 404 are both arranged in the form of an array ofsampled values.

In this example, the input set of sampled values 403 is arranged as amatrix with four rows and one column. In this example, the sampled valuein the first position in the input set of sampled values 403 is thesampled value in the first row and first column of the input set ofsampled values 403. In this example, the sampled value in the secondposition in the input set of sampled values 403 is the sampled value inthe second row and first column of the input set of sampled values 403.In this example, the sampled value in the third position in the inputset of sampled values 403 is the sampled value in the third row andfirst column of the input set of sampled values 403. In this example,the sampled value in the fourth position in the input set of sampledvalues 403 is the sampled value in the fourth row and first column ofthe input set of sampled values 403.

In this example, the output set of sampled values 404 is arranged as amatrix with two rows and two columns. In this example, the number ofsampled values in the output set of sampled values 404 is the same asthe number of sampled values in the input set of sampled values 403,namely four. However, the arrangement of the sampled values is differentin the output set of sampled values 404 compared to that in the inputset of sampled values 403. In this example, the sampled value in thefirst position in the output set of sampled values 404 is the sampledvalue in the first row and first column of the output set of sampledvalues 404. In this example, the sampled value in the second position inthe output set of sampled values 404 is the sampled value in the firstrow and second column of the output set of sampled values 404. In thisexample, the sampled value in the third position in the output set ofsampled values 404 is the sampled value in the second row and firstcolumn of the output set of sampled values 404. In this example, thesampled value in the fourth position in the output set of sampled values404 is the sampled value in the second row and second column of theoutput set of sampled values 404.

In this example, the mapper 402 has access to a database 405. Thedatabase 405 stores data comprising comprises at least one mapping rule.

A mapping rule defines a correspondence between a position of a givensampled value SV₁, SV₂, SV₃, SV₄ in the input set of sampled values 403and the position of the given sampled value SV₁, SV₂, SV₃, SV₄ in theoutput set of sampled values 404.

As such, a mapping rule relates data derived from output data obtainedfrom a given sensor in the plurality of sensors to the identity of thegiven sensor. A mapping rule may be used to exploit statisticalcorrelation between different sensors in the plurality of sensors, forexample by arranging data derived from the different sensors accordingto such a mapping rule. By exploiting correlation between differentsensors, efficiency of coding the data derived from sensor output datamay be improved. Coding efficiency may be particularly improved in caseswhere there are a relatively large number of sensors, for example in anaircraft.

In this example, the mapper 402 is configured to determine a position ofa given sampled value SV₁, SV₂, SV₃, SV₄ in the output set of sampledvalues 404 based on the mapping rule.

In this example, the mapping rule is configured so that the position ofa given sampled value SV₁, SV₂, SV₃, SV₄ in the output set of sampledvalues 404 is the same as the position of the given sampled value SV₁,SV₂, SV₃, SV₄ in the input set of sampled values 403.

In this example, sensors S₁, S₂, S₃, S₄ are arranged in a given order.The given order, in this example, is that sensor S₁ is a first sensor,sensor S₂ is a second sensor, sensor S₃ is a third sensor and sensor S₄is a fourth sensor. In this example, the mapping rule is configured topreserve an order of the sampled values SV₁, SV₂, SV₃, SV₄ in the outputset of sampled values 404 with respect to the given order of the sensorsS₁, S₂, S₃, S₄ from which they are obtained. It will be appreciated thatthe order of the sensors S₁, S₂, S₃, S₄ may be a logical order ratherthan a physical order in which the sensors S₁, S₂, S₃, S₄ are located,for example in a vehicle. For example, the sensors S₁, S₂, S₃, S₄ may beassociated with respective sensor identifiers “1”, “2”, “3”, “4” from alowest sensor identifier “1” to a highest sensor identifier “4” and theorder of the sensors S₁, S₂, S₃, S₄ is the sensor S₁ associated with thelowest sensor identifier “1”, followed by the sensor S₂ associated withthe second-lowest sensor identifier “2” and so on up to the sensor S₄associated with the highest sensor identifier “4”. Although an exampleis provided of numeric sensor identifiers, it will be appreciated thatsensors identifiers may take a different form, such as alphanumeric.

In this example, the first sampled value SV₁ obtained from the firstsensor S₁ is in the first position in both the input set of sampledvalues 403 and the output set of sampled values 404. In this example,the second sampled value SV₂ obtained from the second sensor S₂ is inthe second position in both the input set of sampled values 403 and theoutput set of sampled values 404. In this example, the third sampledvalue SV₃ obtained from the third sensor S₃ is in the third position inboth the input set of sampled values 403 and the output set of sampledvalues 404. In this example, the fourth sampled value SV₄ obtained fromthe fourth sensor S₄ is in the fourth position in both the input set ofsampled values 403 and the output set of sampled values 404.

In this example, an identity of a given sensor S₁, S₂, S₃, S₄ from whicha given sampled value SV₁, SV₂, SV₃, SV₄ in the output set of sampledvalues 404 is obtained is determinable solely from a position of thegiven sampled value SV₁, SV₂, SV₃, SV₄ in the output set of sampledvalues 404. In particular, since, in this example, the mapping rule isconfigured to preserve an order of the sampled values SV₁, SV₂, SV₃, SV₄in the output set of sampled values 404 with respect to the given orderof the sensors S₁, S₂, S₃, S₄ from which they are obtained, it can bedetermined, solely from the position of a given sampled value SV₁, SV₂,SV₃, SV₄ in the output set of sampled values 404 an identity of a givensensor S₁, S₂, S₃, S₄ from which the given sampled value SV₁, SV₂, SV₃,SV₄ is obtained.

For example, it may be determined that the first sampled value SV₁ isobtained from the first sensor S₁ solely on the basis that the firstsampled value SV₁ is in the first position in the output set of sampledvalues 404. It may also be determined that the second sampled value SV₂is obtained from the second sensor S₂ solely on the basis that thesecond sampled value SV₂ is in the second position in the output set ofsampled values 404. It may also be determined that the third sampledvalue SV₃ is obtained from the third sensor S₃ solely on the basis thatthe third sampled value SV₃ is in the third position in the output setof sampled values 404. It may also be determined that the fourth sampledvalue SV₄ is obtained from the fourth sensor S₄ solely on the basis thatthe fourth sampled value SV₄ is in the fourth position in the output setof sampled values 404.

This type of mapping rule is referred to herein as a “fixed” mappingrule. In a mapping using the fixed mapping rule, the position of asampled value SV₁, SV₂, SV₃, SV₄ in a set of sampled values directlyrelates to the identity of the sensor S₁, S₂, S₃, S₄ from which thesampled value SV₁, SV₂, SV₃, SV₄ is obtained. For example, using thefixed mapping rule, it can be directly determined that the sampled valueSV₁ in the first position in the output set of sampled values 404 isobtained from the first sensor S₁, the sampled value SV₂ in the secondposition in the output set of sampled values 404 is obtained from thesecond sensor S₂, the sampled value SV₃ in the third position in theoutput set of sampled values 404 is obtained from the third sensor S₃,and the sampled value SV₄ in the fourth position in the output set ofsampled values 404 is obtained from the fourth sensor S₄.

Although, in this example, the input data 403 has been described ascomprising sampled values SV₁, SV₂, SV₃, SV₄, the mapper 402 mayalternatively or additionally be used to map input data comprising a setof data elements to output data comprising a set of data elements.

Referring to FIG. 5, there is shown schematically an illustration of anexample of a method of processing telemetry data.

A sampled value obtained from a sensor having an identity “i” in theplurality of sensors at a k^(th) sampling time is denoted SV_(i)(t_(k)).A data element indicative of a measure of a change between a sampledvalue obtained from a sensor having an identity “i” at a k^(th) samplingtime and a sampled value obtained from the i^(th) sensor at the k+1^(st)sampling time is denoted ΔSV_(i).

A first set of sampled values 500 is arranged as an array. In thisexample, the first set of sampled values 500 is arranged as atwo-dimensional array having two rows and two columns. The first set ofsampled values 500 includes a first sampled value, SV₁(t₁), obtainedfrom a first sensor S₁ at a first sampling time, t₁. The first set ofsampled values 500 further includes a second sampled value, SV₁(t₁),obtained from a second sensor S₂ at the first sampling time, t₁. Thefirst set of sampled values 500 further includes a third sampled value,SV₁(t₁), obtained from a third sensor S₃ at the first sampling time, t₁.The first set of sampled values 500 further includes a fourth sampledvalue, SV₁(t₁), obtained from a fourth sensor S₄ at the first samplingtime, t₁.

A second set of sampled values 501 is arranged as an array. In thisexample, the second set of sampled values 501 is arranged as atwo-dimensional array having two rows and two columns. The second set ofsampled values 501 includes a first sampled value, SV₁(t₂), obtainedfrom the first sensor S₁ at a second sampling time, t₂. The second setof sampled values 501 further includes a second sampled value, SV₂(t₂),obtained from the second sensor S₂ at the second sampling time, t₂. Thesecond set of sampled values 501 further includes a third sampled value,SV₃(t₂), obtained from the third sensor S₃ at the second sampling time,t₂. The second set of sampled values 501 further includes a fourthsampled value, SV₄(t₂), obtained from the fourth sensor S₄ at the secondsampling time, t₂.

A set of data elements 502 is arranged as an array. In this example, theset of data elements 502 is arranged as a two-dimensional array havingtwo rows and two columns. The set of data elements 502 includes a firstdata element, ΔSV₁, indicative of a measure of a change between thesampled value, SV₁(t₁), obtained from the first sensor S₁ at the firstsampling time, t₁, and the sampled value, SV₁(t₂), obtained from thefirst sensor S₁ at the second sampling time, t₂. The set of dataelements 502 further includes a second data element, ΔSV₂, indicative ofa measure of a change between the sampled value, SV₂(t₁), obtained fromthe second sensor S₂ at the first sampling time, t₁, and the sampledvalue, SV₂(t₂), obtained from the second sensor S₂ at the secondsampling time, t₂. The set of data elements 502 further includes a thirddata element, ΔSV₃, indicative of a measure of a change between thesampled value, SV₃(t₁), obtained from the third sensor S₃ at the firstsampling time, t₁, and the sampled value, SV₃(t₂), obtained from thethird sensor S₃ at the second sampling time, t₂. The set of dataelements 502 further includes a fourth data element, ΔSV₄, indicative ofa measure of a change between the sampled value, SV₄(t₁), obtained fromthe fourth sensor S₄ at the first sampling time, t₁, and the sampledvalue, SV₄(t₂), obtained from the fourth sensor S₄ at the secondsampling time, t₂.

In this example, the first set of sampled values 500, the second set ofsampled values 501 and the set of data elements 502 is each arranged tocreate a virtual plane of sampled values or data elements. In thisexample, the virtual plane is a two-dimensional plane. However, thesampled values or data elements could be arranged in an array, or avirtual plane or arrangement, having more than two dimensions.

In this example, the first set of sampled values 500, the second set ofsampled values 501, and the set of data elements 502 are arranged as thesame type of array, namely in the form of a 2×2 matrix.

Referring to FIG. 6, there is shown schematically an illustration of anexample of a method of processing telemetry data.

In this example, a remote data processing unit has received a first setof sampled values 600 and a set of data elements 601 from a dataprocessing device. The remote data processing unit has decoded and/ordecrypted the received data as needed.

The remote data processing unit is able to recover a second set ofsampled values 602 using the received first set of sampled values 600and the received set of data elements 601. In this example, the remotedata processing unit does not receive the second set of sampled values602 or an encoded version of the second set of sampled values 602 fromthe data processing device, but uses the received first set of sampledvalues 600 and the received set of data elements 601 to recover thesecond set of sampled values 602.

The remote data processing unit recovers the sampled value, SV₁(t₂),obtained from the first sensor S₁ at the second sampling time, t₂, byadding the first data element, ΔSV₁, to the sampled value, SV₁(t₁),obtained from the first sensor S₁ at the first sampling time, t₁. Theremote data processing unit recovers the sampled value, SV₂(t₂),obtained from the second sensor S₂ at the second sampling time, t₂, byadding the second data element, ΔSV₂, to the sampled value, SV₂(t₁),obtained from the second sensor S₂ at the first sampling time, t₁. Theremote data processing unit recovers the sampled value, SV₃(t₂),obtained from the third sensor S₃ at the second sampling time, t₂, byadding the third data element, ΔSV₃, to the sampled value, SV₃(t₁),obtained from the third sensor S₃ at the first sampling time, t₁. Theremote data processing unit recovers the sampled value, SV₄(t₂),obtained from the fourth sensor S₄ at the second sampling time, t₂, byadding the fourth data element, ΔSV₄, to the sampled value, S₄(t₁),obtained from the fourth sensor S₄ at the first sampling time, t₁.

It can be seen from the examples described above with reference to FIGS.5 and 6 that the differences between the sampled values in the first setof values and the second set of sampled values are not treated as randomnoise between subsequent sampling times, but rather as data that isrecorded and used by the remote data processing unit to obtain thesecond set of sampled values. This represents a difference over at leastsome existing image compression techniques, which would see thevariations between the sampled values in the first and second sets ofsampled values as random noise rather than as data to be recorded and tobe used to obtain the second set of sampled values. Existing compressionalgorithms would instead likely ignore such variations. This is becausethe variations are likely to be small, and because existing compressionalgorithms may be optimized to minimize inter-frame compression, forexample variations between frames, based on moving objects in a videosequence.

Accordingly, intra-frame only encoding, in which each set of sampledvalues is encoded separately, may be used if existing image compressionalgorithms were to be used. This, for known image compressionalgorithms, would mean encoding each set of sampled values fully andtransmitting the encoded sets of sampled values to the remote dataprocessing unit.

In contrast, in accordance with examples described herein, suchvariations between the sampled values in the first and second sets ofsampled values are encoded and output for transmission to the remotedata processing unit. In some examples, the second set of sampled valuesis not output for transmission to the remote data processing unit. Thefirst set of sampled values may be encoded using a hierarchical encodingtechnique, for example as described below with reference to FIGS. 17 and18, or using another, for example an existing, encoding technique.However, for the subsequent data to be encoded, namely the set of dataelements, the subsequent data may be seen as an “enhancement” layer overa “base” layer corresponding to the first set of sampled values. Thesequence of snapshots of the sampled values at different times may beseen as different layers in a hierarchical encoding architecture. Forexample, an initial snapshot may be seen as an initial base layer andone or more further snapshots may be seen as one or more enhancementlayers of the base layer. This allows the application of a hierarchicalapproach over this sequence of snapshots. Where a hierarchical approachis used for encoding video data, each image in the video sequence mayhave a base layer and one or more enhancement layers. In other words, insuch a hierarchical approach for encoding video data, each enhancementlayer is a spatial enhancement of the base layer, namely it enhances theimage over the same space at a fixed instant. By applying such ahierarchical encoding technique to the present scenario, temporalenhancement may be applied in relation to the base layer, namelycorresponding to the measures of difference between a snapshot and oneor more previous snapshots as if it was equivalent to a spatialenhancement. When a decoder decodes and combines the base layer—thefirst set of data elements—and the enhancement layer—the set of dataelements—it would obtain the decoded second set of sampled values at thesecond sampling time. This may represent a significant saving in termsof the amount of data to be encoded, since the first set of sampledvalues could be fully encoded and then only an enhancement layer,corresponding to a set of data elements, for the next set or sets ofsampled values could be encoded. This may result in the same outcome asif each set of sampled values had been encoded individually, namely thatthe remote data processing unit can recover the sets of sampled values,but with a reduction in the amount of data that is encoded andtransmitted to be able to do so.

Referring to FIG. 7, there is shown schematically an illustration of anexample of a method of processing telemetry data.

The schematic illustration in FIG. 7 corresponds to the schematicillustration shown in FIG. 5. However, FIG. 7 indicates sampled valuesand data elements based on the examples shown in FIGS. 2 and 3, and theexample of the fixed mapping rule described above with reference to FIG.4.

A first set of sampled values 700 is arranged as an array. In thisexample, the first set of sampled values 700 is arranged as atwo-dimensional array having two rows and two columns. The first set ofsampled values 700 includes a first sampled value, “10”, obtained fromthe first sensor S₁ at the first sampling time, t₁. The first set ofsampled values 700 further includes a second sampled value, “−2”,obtained from the second sensor S₂ at the first sampling time, t₁. Thefirst set of sampled values 700 further includes a third sampled value,“1”, obtained from the third sensor S₃ at the first sampling time, t₁.The first set of sampled values 700 further includes a fourth sampledvalue, “22”, obtained from the fourth sensor S₄ at the first samplingtime, t₁.

A second set of sampled values 701 is arranged as an array. In thisexample, the second set of sampled values 701 is arranged as atwo-dimensional array having two rows and two columns. The second set ofsampled values 701 includes a first sampled value, “10”, obtained fromthe first sensor S₁ at the second sampling time, t₂. The second set ofsampled values 701 further includes a second sampled value, “−2”,obtained from the second sensor S₂ at the second sampling time, t₂. Thesecond set of sampled values 701 further includes a third sampled value,“2”, obtained from the third sensor S₃ at the second sampling time, t₂.The second set of sampled values 701 further includes a fourth sampledvalue, “22”, obtained from the fourth sensor S₄ at the second samplingtime, t₂.

A set of data elements 702 is arranged as an array. In this example, theset of data elements 702 is arranged as a two-dimensional array havingtwo rows and two columns. The set of data elements 702 includes a firstdata element, “0”, indicative of a measure of a change between thesampled value, “10”, obtained from the first sensor S₁ at the firstsampling time, t₁, and the sampled value, “10”, obtained from the firstsensor S₁ at the second sampling time, t₂. The set of data elements 702further includes a second data element, “0”, indicative of a measure ofa change between the sampled value, “−2”, obtained from the secondsensor S₂ at the first sampling time, t₁, and the sampled value, “−2”,obtained from the second sensor S₂ at the second sampling time, t₂. Theset of data elements 702 further includes a third data element, “1”,indicative of a measure of a change between the sampled value, “1”,obtained from the third sensor S₃ at the first sampling time, t₁, andthe sampled value, “2”, obtained from the third sensor S₃ at the secondsampling time, t₂. The set of data elements 702 further includes afourth data element, “0”, indicative of a measure of a change betweenthe sampled value, “22”, obtained from the fourth sensor S₄ at the firstsampling time, t₁, and the sampled value, “22”, obtained from the fourthsensor S₄ at the second sampling time, t₂.

It can readily be seen that the amount of data required to transmit thefirst set of sampled values 700 and the set of data elements 702 is lessthan the amount of data required to transmit the first set of sampledvalues 700 and the second set of sampled values 701. For example, thefirst set of sampled values 700 may require 4*2⁵=128 bits, assuming eachsampled value is represented using 2⁵=32 bits, the second set of sampledvalues 701 may require 4*2⁵=128 bits, assuming each sampled value isrepresented using 2⁵=32 bits, and the set of data elements 702 mayrequire 4*2¹=8 bits, assuming each data element is represented using2¹=2 bits. Transmitting both the first set of sampled values 700 and thesecond set of sampled values 701 would require 128+128=256 bits, whereastransmitting the first set of sampled values 700 and the set of dataelements 702 would require only 128+8=136 bits.

The amount of data to be transmitted can be reduced further by encodingthe first set of sampled values 700 and/or the set of data elements 702prior to transmission, as described in detail herein. Where the changesbetween the sampled values from a sensor are zero or are relativelysmall between the first sampling time, t₁, and the second sampling time,t₂, the values of the data elements in the set of data elements 702 arealso zero or are relatively small values. This can allow efficientencoding of the set of data elements 702 and therefore relatively highcompression rates, particularly compared to techniques in which thefirst set of sampled values 700 and the second set of sampled values 701are both encoded and transmitted to the remote data processing unit.

In this example, the first set of sampled values 700, the second set ofsampled values 701 and the set of data elements 702 are arranged as asame type of array, namely in the form of 2×2 matrices.

It can readily be seen from this example that the amount of datatransmitted from a data processing device to a remote data processingunit depends on various different factors. One factor is the number ofsensors for which data is being reported. Another factor is the size ofthe sampled values. Another factor is the type of encoding techniqueused. Another factor is the extent to which the output data from thesensors changes between sampling times.

As a consequence, the bit rate of the data transmitted from the dataprocessing device to the remote data processing unit may be variable.For example, where the changes of sampled values between differentsampling times are zero or relatively small, the amount of datatransmitted may be relatively low and where the changes of sampledvalues between different sampling times are relatively high, the amountof data transmitted may be relatively high.

In some examples, one or more known characteristics of a communicationchannel between the data processing device and the remote dataprocessing unit is used to constrain or define at least one of thosefactors affecting the bit rate of the transmitted data. Examples of theknown characteristic include, but are not limited to, the capacity ofthe communication channel and the cost of sending data via thecommunication channel.

In some examples, the one or more known characteristics of thecommunication channel are used to determine at least one feature of anencoding technique used to encode the set of data elements. Examples ofthe at least one feature include, but are not limited to, complexity,compression rate, encoding technique, amount of acceptable informationloss during encoding etc.

Referring to FIG. 8, there is shown schematically an illustration of anexample of a method of processing telemetry data.

The illustration in FIG. 8 corresponds to the illustration shown in FIG.6. However, FIG. 8 indicates sampled values and data elements based onthe examples shown in FIGS. 2 and 3 the example of the fixed mappingrule described above with reference to FIG. 4.

In this example, a remote data processing unit has received a first setof sampled values 800 and a set of data elements 801 from a dataprocessing device. The remote data processing unit has decoded and/ordecrypted the received data as needed.

The remote data processing unit is able to recover a second set ofsampled values 802 using the received first set of sampled values 800and the received set of data elements 801. In this example, the remotedata processing unit does not receive the second set of sampled values802 or an encoded version of the second set of sampled values 802 fromthe data processing device, but uses the received first set of sampledvalues 800 and the received set of data elements 801 to recover thesecond set of sampled values 802.

The remote data processing unit recovers the sampled value, “10”,obtained from the first sensor S₁ at the second sampling time, t₂, byadding the first data element value, “0”, to the sampled value, “10”,obtained from the first sensor S₁ at the first sampling time, t₁. Theremote data processing unit recovers the sampled value, “−2”, obtainedfrom the second sensor S₂ at the second sampling time, t₂, by adding thesecond data element value, “0”, to the sampled value, “−2”, obtainedfrom the second sensor S₂ at the first sampling time, t₁. The remotedata processing unit recovers the sampled value, “2”, obtained from thethird sensor S₃ at the second sampling time, t₂, by adding the thirddata element value, “1”, to the sampled value, “1”, obtained from thethird sensor S₃ at the first sampling time, t₁. The remote dataprocessing unit recovers the sampled value, “22”, obtained from thefourth sensor S₄ at the second sampling time, t₂, by adding the fourthdata element value, “0”, to the sampled value, “22”, obtained from thefourth sensor S₄ at the first sampling time, t₁.

Referring to FIG. 9, there is shown a schematic block diagram of anexample of a data processing system 900. The data processing system 900is substantially the same as the data processing system 400 describedabove with reference to FIG. 4 and corresponding entities are shown inFIG. 9 using the same reference numeral as in FIG. 4 but incremented by500. However, compared to data processing system 400 described abovewith reference to FIG. 4, a different mapping rule is used in thisexample and the position of the sampled values SV₁, SV₂, SV₃, SV₄ in theoutput set of sampled values 904 is different.

In this example, the mapping rule is configured so that the position ofa given sampled value SV₁, SV₂, SV₃, SV₄ in the output set of sampledvalues 904 is allowed to be different from the position of the givensampled value SV₁, SV₂, SV₃, SV₄ in the input set of sampled values 903.

In this example, the first sampled value SV₁ obtained from the firstsensor S₁ is in the first position in both the input set of sampledvalues 903 and the output set of sampled values 904. In this example,the second sampled value SV₂ obtained from the second sensor S₂ is inthe second position in both the input set of sampled values 903 and theoutput set of sampled values 904. In this example, the third sampledvalue SV₃ obtained from the third sensor S₃ is in the third position inthe input set of sampled values 903 but is in the fourth position in theoutput set of sampled values 904. In this example, the fourth sampledvalue SV₄ obtained from the fourth sensor S₄ is in the fourth positionin the input set of sampled values 903 but is in the third position inthe output set of sampled values 904.

In this example, an identity of a given sensor S₁, S₂, S₃, S₄ from whicha given sampled value SV₁, SV₂, SV₃, SV₄ in the output set of sampledvalues 904 is obtained is indeterminable solely from a position of thegiven sampled value SV₁, SV₂, SV₃, SV₄ in the output set of sampledvalues 904.

In particular, since, in this example, the mapping rule is configured toallow an order of the sampled values SV₁, SV₂, SV₃, SV₄ in the outputset of sampled values 904 to be different from the given order of thesensors S₁, S₂, S₃, S₄ from which they are obtained, it cannot bedetermined, solely from the position of a given sampled value SV₁, SV₂,SV₃, SV₄ in the output set of sampled values 904, an identity of a givensensor S₁, S₂, S₃, S₄ from which the given sampled value SV₁, SV₂, SV₃,SV₄ is obtained.

For example, the sampled value SV₃ obtained from the third sensor S₃ isin the fourth position in the output set of sampled values 904. Further,the sampled value SV₄ obtained from the fourth sensor S₄ is in the thirdposition in the output set of sampled values 904.

In this example, the data processing device 901 is configured to output,for transmission to a remote data processing unit, correlation dataassociating the given sampled values SV₁, SV₂, SV₃, SV₄ in the outputset of sampled values 904 with the given sensors S₁, S₂, S₃, S₄ fromwhich they are obtained. The correlation data may, for example, beincluded in header information in data transmitted to the remote dataprocessing unit.

In such examples, a sensor identifier of a given sensor from which agiven sampled value is obtained is associated with the given sampledvalue in the correlation data. The correlation data allows the givensensor from which the given sampled value is obtained to be determined,since the sensor identity is indeterminable based solely on knowing aposition of the given sampled value in the output set of sampled values904.

In this example, the mapper 902 is configured to determine a positionfor at least some sampled values SV₁, SV₂, SV₃, SV₄ in the output set ofsampled values 904 based on a measure of a variation of the output datafrom the sensors S₁, S₂, S₃, S₄ from which they are obtained.

In this example, sampled values SV₁, SV₂, SV₃, SV₄ obtained from sensorsS₁, S₂, S₃, S₄ whose output data changes relatively frequently, comparedto sensors S₁, S₂, S₃, S₄ whose output data changes relativelyinfrequently, are grouped together in the output set of sampled values904. In this example, and with reference to FIG. 2, it can be seen thatthe output, O(S₃), of the third sensor S₃ changes relatively frequentlycompared to the outputs, O(S₁), O(S₂), O(S₄), of the first sensor S₁,the second sensor S₂ and the fourth sensor S₄ respectively. For example,the rate of change of the outputs, O(S₁), O(S₂), O(S₄), of the firstsensor S₁, the second sensor S₂ and the fourth sensor S₄ respectively iszero across the time period shown in FIG. 2, whereas the rate of changeof the output, O(S₃), of the third sensor S₃ is a non-zero, positivevalue across the time period shown in FIG. 2.

In this example, sampled values SV₁, SV₂, SV₃, SV₄ obtained from sensorsS₁, S₂, S₃, S₄ whose output data changes relatively infrequentlycompared to sensors S₁, S₂, S₃, S₄ whose output data changes relativelyfrequently are grouped together in the output set of sampled values 904.In this example, and with reference to FIG. 2, it can be seen that theoutput data, O(S₁), O(S₂), O(S₄), of the first sensor S₁, the secondsensor S₂ and the fourth sensor S₄ respectively changes relativelyinfrequently compared to the output data, O(S₃), of the third sensor S₃across the time period shown in FIG. 2. As such, in this example, thesampled value SV₃ obtained from the third sensor S₃ is grouped by itselfand the sampled values SV₁, SV₂, SV₄ obtained from the first sensor S₁,the second sensor S₂ and the fourth sensor S₄ respectively are groupedtogether in the output set of sampled values 904.

This type of mapping rule is referred to herein as a “dynamic” mappingrule. In a mapping using the dynamic mapping rule, the sampled valueswithin a set of sampled values are ordered based on at least onecriterion other than the identity of the sensor from which they areobtained.

As indicated above, in some examples, such a criterion relates to howfrequently the output data from the given sensor S₁, S₂, S₃, S₄ changes.In some examples, sampled values from sensors S₁, S₂, S₃, S₄ whoseoutput data values change often are mapped so as to be concentratedclose to each other in the output set of sampled values 904. Byconcentrating such sampled values in this way, compression of the outputset of sampled values 904 may be improved. This is because clusters ofsimilar sampled values are encoded more efficiently than if they weresparsely dispersed in the output set of sampled values 904.

In an example, sampled values of sensors S₁, S₂, S₃, S₄ which are morelikely to change frequently than others are mapped to one or morespecific areas of the output set of sampled values 904. An assessment ofthe likelihood of change may be based, for example, on historical setsof data elements, where the data elements are indicative of a measure ofa change between a sampled value obtained from a sensor at a firstsampling time and a corresponding sampled value obtained from the samesensors at a second sampling time.

In some examples, the mapping rule used by the mapper is allowed to varyover time. For example, the mapper may use a fixed mapping rule for afirst mapping time period and a dynamic mapping rule for a second,different time period. In some examples, the data processing device andthe remote data processing unit communicate data to indicate whichmapping rule is being used. In some examples, an indication of whichmapping rule is being used may be implicit in the data transmitted tothe remote data processing unit. For example, it may be considered to beimplicit that a dynamic mapping rule is being used by the mapper whenthe remote data processing unit receives correlation data.

Although, in this example, the input data 903 has been described ascomprising sampled values SV₁, SV₂, SV₃, SV₄, the mapper 902 mayalternatively or additionally be used to map input data comprising a setof data elements to output data comprising a set of data elements.

Referring to FIG. 10, there is shown schematically an illustration of anexample of a method of processing telemetry data.

A first set of sampled values 1000 is arranged as an array. In thisexample, the first set of sampled values 1000 is arranged as atwo-dimensional array having two rows and two columns. The first set ofsampled values 1000 includes a first sampled value, SV₁(t₁), obtainedfrom a first sensor S₁ at a first sampling time, t₁. The first set ofsampled values 1000 further includes a second sampled value, SV₂(t₁),obtained from a second sensor S₂ at the first sampling time, t₁. Thefirst set of sampled values 1000 further includes a third sampled value,SV₄(t₁), obtained from a fourth sensor S₄ at the first sampling time,t₁. The first set of sampled values 1000 further includes a fourthsampled value, SV₃(t₁), obtained from a third sensor S₃ at the firstsampling time, t₁.

A second set of sampled values 1001 is arranged as an array. In thisexample, the second set of sampled values 1001 is arranged as atwo-dimensional array having two rows and two columns. The second set ofsampled values 1001 includes a first sampled value, SV₁(t₂), obtainedfrom the first sensor S₁ at a second sampling time, t₂. The second setof sampled values 1001 further includes a second sampled value, SV₂(t₂),obtained from the second sensor S₂ at the second sampling time, t₂. Thesecond set of sampled values 1001 further includes a third sampledvalue, SV₄(t₂), obtained from the fourth sensor S₄ at the secondsampling time, t₂. The second set of sampled values 1001 furtherincludes a fourth sampled value, SV₃(t₂), obtained from the third sensorS₃ at the second sampling time, t₂.

A set of data elements 1002 is arranged as an array. In this example,the set of data elements 1002 is arranged as a two-dimensional arrayhaving two rows and two columns. The set of data elements 1002 includesa first data element, ΔSV₁, indicative of a measure of a change betweenthe sampled value, SV₁(t₁), obtained from the first sensor S₁ at thefirst sampling time, t₁, and the sampled value, SV₁(t₂), obtained fromthe first sensor S₁ at the second sampling time, t₂. The set of dataelements 1002 further includes a second data element, ΔSV₂, indicativeof a measure of a change between the sampled value, SV₂(t₁), obtainedfrom the second sensor S₂ at the first sampling time, t₁, and thesampled value, SV₂(t₂), obtained from the second sensor S₂ at the secondsampling time, t₂. The set of data elements 1002 further includes athird data element, ΔSV₄, indicative of a measure of a change betweenthe sampled value, SV₄(t₁), obtained from the fourth sensor S₄ at thefirst sampling time, t₁, and the sampled value, SV₄(t₂), obtained fromthe fourth sensor S₄ at the second sampling time, t₂. The set of dataelements 1002 further includes a fourth data element, ΔSV₃, indicativeof a measure of a change between the sampled value, SV₃(t₁), obtainedfrom the third sensor S₃ at the first sampling time, t₁, and the sampledvalue, SV₃(t₂), obtained from the third sensor S₃ at the second samplingtime, t₂.

In this example, the first set of sampled values 1000, the second set ofsampled values 1001 and the set of data elements 1002 is each arrangedto create a virtual plane of sampled values or data elements. In thisexample, the virtual plane is a two-dimensional plane. However, thesampled values or data elements could be arranged in an array, or avirtual plane, having more than two dimensions.

In this example, the first set of sampled values 1000, the second set ofsampled values 1001, and the set of data elements 1002 are arranged asthe same type of array, namely in the form of a 2×2 matrix.

Referring to FIG. 11, there is shown schematically an illustration of anexample of a method of processing telemetry data.

In this example, a remote data processing unit has received a first setof sampled values 1100 and a set of data elements 1101 from a dataprocessing device. The remote data processing unit has decoded and/ordecrypted the received data as needed.

The remote data processing unit is able to recover a second set ofsampled values 1102 using the received first set of sampled values 1100and the received set of data elements 1101. In this example, the remotedata processing unit does not receive the second set of sampled values1102 or an encoded version of the second set of sampled values 1102 fromthe data processing device, but uses the received first set of sampledvalues 1100 and the received set of data elements 1101 to recover thesecond set of sampled values 1102.

The remote data processing unit recovers the sampled value, SV₁(t₂),obtained from the first sensor S₁ at the second sampling time, t₂, byadding the first data element, ΔSV₁, to the sampled value, SV₁(t₁),obtained from the first sensor S₁ at the first sampling time, t₁. Theremote data processing unit recovers the sampled value, SV₂(t₂),obtained from the second sensor S₂ at the second sampling time, t₂, byadding the second data element, ΔSV₂, to the sampled value, SV₂(t₁),obtained from the second sensor S₂ at the first sampling time, t₁. Theremote data processing unit recovers the sampled value, SV₄(t₂),obtained from the fourth sensor S₄ at the second sampling time, t₂, byadding the third data element, ΔSV₄, to the sampled value, SV₄(t₁),obtained from the fourth sensor S₄ at the first sampling time, t₁. Theremote data processing unit recovers the sampled value, SV₃(t₂),obtained from the third sensor S₃ at the second sampling time, t₂, byadding the third data element, ΔSV₃, to the sampled value, S₃(t₁),obtained from the third sensor S₃ at the first sampling time, t₁.

Referring to FIG. 12, there is shown schematically an illustration of anexample of a method of processing telemetry data.

The schematic illustration in FIG. 12 corresponds to the schematicillustration shown in FIG. 10. However, FIG. 12 indicates sampled valuesand data elements based on the examples shown in FIGS. 2 and 3, and theexample of the dynamic mapping rule described above with reference toFIG. 9.

A first set of sampled values 1200 is arranged as an array. In thisexample, the first set of sampled values 1200 is arranged as atwo-dimensional array having two rows and two columns. The first set ofsampled values 1200 includes a first sampled value, “10”, obtained fromthe first sensor S₁ at the first sampling time, t₁. The first set ofsampled values 1200 further includes a second sampled value, “−2”,obtained from the second sensor S₂ at the first sampling time, t₁. Thefirst set of sampled values 1200 further includes a third sampled value,“22”, obtained from the fourth sensor S₄ at the first sampling time, t₁.The first set of sampled values 1200 further includes a fourth sampledvalue, “1”, obtained from the third sensor S₃ at the first samplingtime, t₁.

A second set of sampled values 1201 is arranged as an array. In thisexample, the second set of sampled values 1201 is arranged as atwo-dimensional array having two rows and two columns. The second set ofsampled values 1201 includes a first sampled value, “10”, obtained fromthe first sensor S₁ at the second sampling time, t₂. The second set ofsampled values 1201 further includes a second sampled value, “−2”,obtained from the second sensor S₂ at the second sampling time, t₂. Thesecond set of sampled values 1201 further includes a third sampledvalue, “22”, obtained from the fourth sensor S₄ at the second samplingtime, t₂. The second set of sampled values 1201 further includes afourth sampled value, “2”, obtained from the third sensor S₃ at thesecond sampling time, t₂.

A set of data elements 1202 is arranged as an array. In this example,the set of data elements 1202 is arranged as a two-dimensional arrayhaving two rows and two columns. The set of data elements 1202 includesa first data element, “0”, indicative of a measure of a change betweenthe sampled value, “10”, obtained from the first sensor S₁ at the firstsampling time, t₁, and the sampled value, “10”, obtained from the firstsensor S₁ at the second sampling time, t₂. The set of data elements 1202further includes a second data element, “0”, indicative of a measure ofa change between the sampled value, “−2”, obtained from the secondsensor S₂ at the first sampling time, t₁, and the sampled value, “−2”,obtained from the second sensor S₂ at the second sampling time, t₂. Theset of data elements 1202 further includes a third data element, “0”,indicative of a measure of a change between the sampled value, “22”,obtained from the fourth sensor S₄ at the first sampling time, t₁, andthe sampled value, “22”, obtained from the fourth sensor S₄ at thesecond sampling time, t₂. The set of data elements 1202 further includesa fourth data element, “1”, indicative of a measure of a change betweenthe sampled value, “1”, obtained from the third sensor S₃ at the firstsampling time, t₁, and the sampled value, “2”, obtained from the thirdsensor S₃ at the second sampling time, t₂.

In this example, the first set of sampled values 1200, the second set ofsampled values 1201 and the set of data elements 1202 are arranged as asame type of array, namely in the form of 2×2 matrices.

Referring to FIG. 13, there is shown schematically an illustration of anexample of a method of processing telemetry data.

The illustration in FIG. 13 corresponds to the illustration shown inFIG. 11. However, FIG. 13 indicates sampled values and data elementsbased on the examples shown in FIGS. 2 and 3 the example of the dynamicmapping rule described above with reference to FIG. 9.

In this example, a remote data processing unit has received a first setof sampled values 1300 and a set of data elements 1301 from a dataprocessing device. The remote data processing unit has decoded and/ordecrypted the received data as needed.

The remote data processing unit is able to recover a second set ofsampled values 1302 using the received first set of sampled values 1300and the received set of data elements 1301. In this example, the remotedata processing unit does not receive the second set of sampled values1302 or an encoded version of the second set of sampled values 1302 fromthe data processing device, but uses the received first set of sampledvalues 1300 and the received set of data elements 1301 to recover thesecond set of sampled values 1302.

The remote data processing unit recovers the sampled value, “10”,obtained from the first sensor S₁ at the second sampling time, t₂, byadding the first data element value, “0”, to the sampled value, “10”,obtained from the first sensor S₁ at the first sampling time, t₁. Theremote data processing unit recovers the sampled value, “−2”, obtainedfrom the second sensor S₂ at the second sampling time, t₂, by adding thesecond data element value, “0”, to the sampled value, “−2”, obtainedfrom the second sensor S₂ at the first sampling time, t₁. The remotedata processing unit recovers the sampled value, “22”, obtained from thefourth sensor S₄ at the second sampling time, t₂, by adding the thirddata element value, “0”, to the sampled value, “22”, obtained from thefourth sensor S₄ at the first sampling time, t₁. The remote dataprocessing unit recovers the sampled value, “2”, obtained from the thirdsensor S₃ at the second sampling time, t₂, by adding the fourth dataelement value, “1”, to the sampled value, “1”, obtained from the thirdsensor S₃ at the first sampling time, t₁.

Referring to FIG. 14, there is shown a series of graphs illustratingexamples output data from a plurality of sensors associated with avehicle.

In this example, the example output data is the same as the output datashown in FIG. 2. Corresponding graphs are shown in FIG. 14 using thesame reference numerals as those used in FIG. 2, but incremented by1200.

In this example, the output data from the first sensor S₁, the secondsensor S₂, and the fourth sensor S₄ is sampled at a first sampling rate.In this example, the first sampling rate is indicated by the differencebetween the first sampling time, t₁, and the second sampling time, t₂.

In this example, the output data from the third sensor S₃ is sampled ata second sampling rate. In this example, the second sampling rate isindicated by the difference between the first sampling time, t₁, and anintermediate sampling time, t_(1.5), and between the intermediatesampling time, t_(1.5), and the second sampling time, t₂. In thisexample, the second sampling rate is higher than the first samplingrate. In this specific example, the second sampling rate is twice thefirst sampling rate. As such, in this example, samples of the outputdata from the third sensor S₃ are obtained twice as often as they arefrom the output data from the first sensor S₁, the second sensor S₂, andthe fourth sensor S₄. In this example, the sampled value obtained fromthird sensor S₃ at the intermediate sampling time, t_(1.5), is “1.5”.

In this example, a data processing device is configured to generate afirst set of sampled values and a second set of sampled values at thesecond, higher sampling rate.

In this example, the first set of sampled values is associated with thefirst sampling time, t₁. In this example, the data processing device isconfigured to generate the first set of sampled values using a sampledvalue “1” obtained using the third sensor S₃ at the first sampling time,t₁. In this example, the data processing device is configured togenerate the first set of sampled values also using sampled valuesobtained from the first sensor S₁, the second sensor S₂, and the fourthsensor S₄, at the first sampling time, t₁, the sampled values being“10”, “−2” and “22” respectively.

In this example, a second set of sampled values is associated with theintermediate sampling time, t_(1.5). In this example, the dataprocessing device is configured to generate the second set of sampledvalues using a sampled value “1.5” obtained using the third sensor S₃ atthe intermediate sampling time, t_(1.5). In this example, the dataprocessing device is configured to generate the second set of sampledvalues also using the sampled values previously obtained from the firstsensor S₁, the second sensor S₂, and the fourth sensor S₄, at the firstsampling time, t₁, the sampled values being “10”, “−2” and “22”respectively. The data processing device is configured to use thepreviously obtained sampled values from the first sensor S₁, the secondsensor S₂, and the fourth sensor S₄, because newer sampled values arenot available from the first sensor S₁, the second sensor S₂, and thefourth sensor S₄, at the intermediate sampling time, t_(1.5).

In this example, a third set of sampled values is associated with thesecond sampling time, t₂. In this example, the data processing device isconfigured to generate the third set of sampled values using a sampledvalue “2” obtained using the third sensor S₃ at the second samplingtime, t₂. In this example, the data processing device is configured togenerate the third set of sampled values also using the sampled valuesobtained from the first sensor S₁, the second sensor S₂, and the fourthsensor S₄, at the second sampling time, t₂, the sampled values being“10”, “−2” and “22” respectively.

As such, sampling may be performed at different sampling rates. Samplingrates may depend, for example, on the type of sensor concerned and/orone or more sensor configuration parameters.

In this example, the rate used for generating the first, second andthird sets of sampled values is the highest sampling rate across all ofthe sensors S₁, S₂, S₃, S₄. If other sensors S₁, S₂, S₃, S₄ are sampledat a lower sampling rate, then the most recent value is used until thenext sample for that sensor S₁, S₂, S₃, S₄ is available.

By sampling at the highest sampling rate across all of the sensors S₁,S₂, S₃, S₄ the sampled values from the one or more sensors S₁, S₂, S₃,S₄ with the highest sampling rates are still reported to the remote dataprocessing unit. If the lowest sampling rate were used instead, some ofthe available sampled values from the one or more sensors S₁, S₂, S₃, S₄with the highest sampling rates may not be reported to the remote dataprocessing unit.

Referring to FIG. 15, there is shown a series of graphs illustratingexamples of output data from a plurality of sensors associated with avehicle.

In this example, the example output data from the first sensor S₁, thethird sensor S₃ and the fourth sensor S₄ is the same as the output datashown in FIG. 2. Graphs that are common to both FIGS. 2 and 15 are shownin FIG. 15 using the same reference numerals as those used in FIG. 2,but incremented by 1300.

However, in this example, the output, O(S₂), of the second sensor S₂ isdifferent from the output, O(S₂), of the second sensor S₂ shown in graph201 in FIG. 2.

Output data from the second sensor S₂ in accordance with this example isshown in a graph 1504. The graph 1504 indicates how the output, O(S₂),of the second sensor S₂ varies over time, t. In this example, theoutput, O(S₂), of the second sensor S₂ starts at a constant value of“−2” and is at the value of “−2” at the first sampling time, t₁. Theoutput, O(S₂), of the second sensor S₂ remains at a constant value of“−2” until a quarter of the way between the first sampling time, t₁, andthe second sampling time, t₂, where is increases linearly. At theintermediate sampling time, t_(1.5), halfway between the first samplingtime, t₁, and the second sampling time, t₂, the output, O(S₂), of thesecond sensor S₂ is “0”. The output, O(S₂), of the second sensor S₂continues to increase linearly until three quarters of the way betweenthe first sampling time, t₁, and the second sampling time, t₂, at whichpoint the output, O(S₂), of the second sensor S₂ is “2”. Subsequently,the output, O(S₂), of the second sensor S₂ remains at a constant valueof “2”.

In this example, the output data from the first sensor S₁, the secondsensor S₂, and the fourth sensor S₄ is sampled at a first sampling rate.In this example, the first sampling rate is indicated by the differencebetween the first sampling time, t₁, and the second sampling time, t₂.

In this example, the output data from the third sensor S₃ is sampled ata second sampling rate. In this example, the second sampling rate isindicated by the difference between the first sampling time, t₁, and anintermediate sampling time, t_(1.5), and between the intermediatesampling time, t_(1.5), and the second sampling time, t₂. In thisexample, the second sampling rate is higher than the first samplingrate. In this specific example, the second sampling rate is twice thefirst sampling rate. As such, in this example, samples of the outputdata from the third sensor S₃ are taken twice as often as they are fromthe output data from the first sensor S₁, the second sensor S₂, and thefourth sensor S₄.

In this example, the data processing device is configured to generate afirst set of sampled values and a second set of sampled values at thesecond, higher sampling rate.

In this example, the first set of sampled values is associated with thefirst sampling time, t₁. In this example, the data processing device isconfigured to generate the first set of sampled values using a sampledvalue “1” obtained using the third sensor S₃ at the first sampling time,t₁. In this example, the data processing device is configured togenerate the first set of sampled values also using sampled valuesobtained from the first sensor S₁, the second sensor S₂, and the fourthsensor S₄, at the first sampling time, t₁, the sampled values being“10”, “−2” and “22” respectively.

In this example, a second set of sampled values is associated with theintermediate sampling time, t_(1.5). In this example, the dataprocessing device is configured to generate the second set of sampledvalues using a sampled value “1.5” obtained using the third sensor S₃ atthe intermediate sampling time, t_(1.5). In this example, the dataprocessing device is configured to generate the second set of sampledvalues also using the sampled values previously obtained from the firstsensor S₁, the second sensor S₂, and the fourth sensor S₄, at the firstsampling time, t₁, the sampled values being “10”, “−2” and “22”respectively. The data processing device is configured to use thepreviously obtained sampled values from the first sensor S₁, the secondsensor S₂, and the fourth sensor S₄, because newer sampled values arenot available from the first sensor S₁, the second sensor S₂, and thefourth sensor S₄, at the intermediate sampling time, t_(1.5). Inparticular, the sampled value in the second set of sampled valuesobtained using the second sensor S₂ is “−2”, even though the actualvalue of the output data of the second sensor S₂ at the intermediatesampling time, t_(1.5) is “0”. This is because the second sensor S₂ issampled at the first sampling time, t₁, and the second sampling time,t₂, but not at the intermediate sampling time, t_(1.5). As such thesampled value “−2” obtained using the second sensor S₂ at the firstsampling time, t₁, is the most recent sampled value available for thesecond sensor S₂ at the intermediate sampling time, t_(1.5).

In this example, a third set of sampled values is associated with thesecond sampling time, t₂. In this example, the data processing device isconfigured to generate the third set of sampled values using a sampledvalue “2” obtained using the third sensor S₃ at the second samplingtime, t₂. In this example, the data processing device is configured togenerate the third set of sampled values also using the sampled valuesobtained from the first sensor S₁, the second sensor S₂, and the fourthsensor S₄, at the second sampling time, t₂, the sampled values being“10”, “−2” and “22” respectively.

Referring to FIG. 16, there is shown a series of graphs illustratingexamples of output data from a plurality of sensors associated with avehicle.

In this example, the example output data is the same as the output datashown in FIG. 15. Corresponding graphs are shown in FIG. 16 using thesame reference numerals as those used in FIG. 15, but incremented by100.

In this example, the output data from the first sensor S₁ and the fourthsensor S₄ is sampled at a first sampling rate. In this example, thefirst sampling rate is indicated by the difference between the firstsampling time, t₁, and the second sampling time, t₂.

In this example, the output data from the third sensor S₃ is sampled ata second sampling rate. In this example, the second sampling rate isindicated by the difference between the first sampling time, t₁, and anintermediate sampling time, t_(1.5), and between the intermediatesampling time, t_(1.5), and the second sampling time, t₂. In thisexample, the second sampling rate is higher than the first samplingrate. In this specific example, the second sampling rate is twice thefirst sampling rate. As such, in this example, samples of the outputdata from the third sensor S₃ are obtained twice as often as they arefrom the output data from the first sensor S₁, and the fourth sensor S₄.In this example, the sampled value obtained from third sensor S₃ at theintermediate sampling time, t_(1.5), is “1.5”.

In this example, the output data from the second sensor S₂ is sampled ata third sampling rate. In this example, the third sampling rate isindicated by the difference between the first sampling time, t₁, and afirst further intermediate sampling time, t_(1.25), the differencebetween the first further intermediate sampling time, t_(1.25), and theintermediate sampling time, t_(1.5), the difference between theintermediate sampling time, t_(1.5), and a second further intermediatesampling time, t_(1.75), and the difference between and between thesecond further intermediate sampling time, t_(1.75), and the secondsampling time, t₂. In this example, the third sampling rate is higherthan the first and second sampling rates. In this specific example, thethird sampling rate is twice the second sampling rate and four times thefirst sampling rate. As such, in this example, samples of the outputdata from the second sensor S₂ are obtained twice as often as they arefrom the output data from the third sensor S₃, and four times as oftenas they are from the first sensor S₁ and the fourth sensor S₄. In thisexample, the sampled value obtained from the second sensor S₂ at thefirst further intermediate sampling time, t_(1.25), is “−2”, at theintermediate sampling time, t_(1.5), is “0” and at the second furtherintermediate sampling time, t_(1.75), is “2”.

In this example, the data processing device is configured to generate asets of sampled values at the third, highest sampling rate.

In this example, the first set of sampled values is associated with thefirst sampling time, t₁. In this example, the data processing device isconfigured to generate the first set of sampled values using a sampledvalue “−2” obtained using the second sensor S₂ at the first samplingtime, t₁, and a sampled value “1” obtained using the third sensor S₃ atthe first sampling time, t₁. In this example, the data processing deviceis configured to generate the first set of sampled values also usingsampled values obtained from the first sensor S₁ and the fourth sensorS₄ at the first sampling time, t₁, the sampled values being “10” and“22” respectively.

In this example, a second set of sampled values is associated with thefirst further intermediate sampling time, t_(1.25). In this example, thedata processing device is configured to generate the second set ofsampled values using a sampled value “−2” obtained using the secondsensor S₂ at the first further intermediate sampling time, t_(1.25). Inthis example, the data processing device is configured to generate thesecond set of sampled values also using the sampled values previouslyobtained from the first sensor S₁, the third sensor S₃, and the fourthsensor S₄, at the first sampling time, t₁, the sampled values being“10”, “1” and “22” respectively. The data processing device isconfigured to use the previously obtained sampled values from the firstsensor S₁, the third sensor S₃, and the fourth sensor S₄, because newersampled values are not available from the first sensor S₁, the thirdsensor S₃, and the fourth sensor S₄, at the first further intermediatesampling time, t_(1.25).

In this example, a third set of sampled values is associated with theintermediate sampling time, t_(1.5). In this example, the dataprocessing device is configured to generate the third set of sampledvalues using a sampled value “0” obtained using the second sensor S₂ atthe intermediate sampling time, t_(1.5), and using a sampled value “1.5”obtained using the third sensor S₃ at the intermediate sampling time,t_(1.5). In this example, the data processing device is configured togenerate the third set of sampled values also using the sampled valuespreviously obtained from the first sensor S₁ and the fourth sensor S₄,at the first sampling time, t₁, the sampled values being “10” and “22”respectively. The data processing device is configured to use thepreviously obtained sampled values from the first sensor S₁ and thefourth sensor S₄, because newer sampled values are not available fromthe first sensor S₁ and the fourth sensor S₄, at the intermediatesampling time, t_(1.5).

In this example, a fourth set of sampled values is associated with thesecond further intermediate sampling time, t_(1.75). In this example,the data processing device is configured to generate the fourth set ofsampled values using a sampled value “2” obtained using the secondsensor S₂ at the second further intermediate sampling time, t_(1.75). Inthis example, the data processing device is configured to generate thefourth set of sampled values also using a sampled value “1.5” obtainedusing the third sensor S₃ at the intermediate sampling time, t_(1.5). Inthis example, the data processing device is configured to generate thefourth set of sampled values also using the sampled values previouslyobtained from the first sensor S₁ and the fourth sensor S₄, at the firstsampling time, t₁, the sampled values being “10” and “22” respectively.The data processing device is configured to use the previously obtainedsampled values from the first sensor S₁, the third sensor S₃, and thefourth sensor S₄, because newer sampled values are not available fromthe first sensor S₁, the third sensor S₃, and the fourth sensor S₄, atthe second further intermediate sampling time, t_(1.75).

In this example, a fifth set of sampled values is associated with thesecond sampling time, t₂. In this example, the data processing device isconfigured to generate the fifth set of sampled values using a sampledvalue “2” obtained using the second sensor S₂ at the second samplingtime, t₂. In this example, the data processing device is configured togenerate the fifth set of sampled values also using a sampled value “2”obtained using the third sensor S₃ at the second sampling time, t₂. Inthis example, the data processing device is configured to generate thefourth set of sampled values also using the sampled values obtained fromthe first sensor S₁ and the fourth sensor S₄ at the second samplingtime, t₂, the sampled values being “10” and “22” respectively.

In this example, five sets of sampled values are generated, each setbeing associated with a different sampling time.

In some examples, a reference set of sampled values serves as areference for deriving data elements associated with a plurality ofsubsequent sets of sampled values. For example, a first set of dataelements may be derived in which a data element is indicative of ameasure of a change between a sampled value in the first set of sampledvalues and a corresponding sampled value in the second set of sampledvalues. A second set of data elements may be derived in which a dataelement is indicative of a measure of a change between a sampled valuein the first set of sampled values and a corresponding sampled value inthe third set of sampled values. A third set of data elements may bederived in which a data element is indicative of a measure of a changebetween a sampled value in the first set of sampled values and acorresponding sampled value in the fourth set of sampled values. Afourth set of data elements may be derived in which a data element isindicative of a measure of a change between a sampled value in the firstset of sampled values and a corresponding sampled value in the fifth setof sampled values.

In some examples, a reference set of sampled values serves as areference for deriving data elements associated with a single subsequentset of sampled values. For example, a first set of data elements may bederived in which a data element is indicative of a measure of a changebetween a sampled value in the first set of sampled values and acorresponding sampled value in the second set of sampled values. Asecond set of data elements may be derived in which a data element isindicative of a measure of a change between a sampled value in thesecond set of sampled values and a corresponding sampled value in thethird set of sampled values. A third set of data elements may be derivedin which a data element is indicative of a measure of a change between asampled value in the third set of sampled values and a correspondingsampled value in the fourth set of sampled values. A fourth set of dataelements may be derived in which a data element is indicative of ameasure of a change between a sampled value in the fourth set of sampledvalues and a corresponding sampled value in the fifth set of sampledvalues.

One or more reference sets of sampled values may be transmittedintermittently to allow the remote data processing unit to synchronizewith the current sampled values obtained by the data processing device.

Referring to FIG. 17, there is shown a schematic block diagram of anexample of a data processing system 1700.

The data processing system 1700 may be used to encode and decode data.Examples of such data include, but are not limited to, the first set ofsampled values and the set of data elements.

The signal processing system 1700 includes a data processing device 1701and a remote data processing unit 1702. In this example, the dataprocessing device 1701 comprises encoder functionality. The encoderfunctionality may be provided by one or more hardware and/or one or moresoftware components. In this example, the remote data processing unit1702 comprises decoding functionality. The decoding functionality may beprovided by one or more hardware and/or one or more software components.

In this example, the data processing system 1700 is used to implement ahierarchical encoding technique, as will now be described.

In this example, the data processing device 1701 receives data to beencoded. The data to be encoded is at a relatively high level of quality1703. For convenience and brevity, in this example, the data to beencoded is in the form of a two-dimensional array of values, it beingunderstood that the data to be encoded may be of a different type. Forexample, the data to be encoded may in the form of a one-dimensionalarray of values, an array of values having more than two dimensions, ordata arranged in a form other than an array.

The data processing system 1700 provides renditions of the data atmultiple different levels of quality (LoQs). In this example, the dataprocessing system 1700 provides renditions of the data at threedifferent levels of quality, it being understood that renditions of thedata at a different number of levels of quality could be provided. Inthis specific example, the data processing system 1700 providesrenditions of the data at relatively high, medium and relatively lowlevels of quality. In some examples, the data processing system 1700provides the renditions of the data at multiple different levels ofquality in a lossless manner. In other words, in such examples, arendition of the data at a higher level of quality can be fullyrecovered from a rendition of the data at a lower level of quality sothat no data is lost by processing the data at different levels ofquality.

In this example, the data processing device 1701 processes the data atthe relatively high level of quality 1703 to produce a rendition of thedata at a medium level of quality 1704 and to produce reconstructiondata 1705. The reconstruction data 1705 indicates how to reconstruct therendition of the data at the relatively high level of quality 1703 usingthe rendition of the data at the medium level of quality 1704.Reconstruction of the data at the relatively high level of quality mayinvolve using other data.

The data processing device 1701 processes the rendition of the datasignal at the medium level of quality 1704 to produce a rendition of thedata at a relatively low level of quality 1706 and to producereconstruction data 1707. The reconstruction data 1707 indicates how toreconstruct the rendition of the data at the medium level of quality1704 using the rendition of the data at the relatively low level ofquality 1706. Reconstruction of the data at the medium level of quality1704 may involve using other data.

The data processing device 1701 generates data 1708 usable to derive therendition of the data at the relatively low level of quality 1706. Thedata 1708 usable to derive the rendition of the data at the relativelylow level of quality 1706 may for example comprise an encrypted versionof the rendition of the data at the relatively low level of quality1706. The remote data processing unit 1702 can then decrypt theencrypted version 1708 of the rendition of the data at the relativelylow level of quality 1706 to derive the rendition of the data at therelatively low level of quality 1706.

The data processing device 1701 transmits the data 1708 usable to derivethe rendition of the data at the relatively low level of quality 1706and the reconstruction data 1705, 1707 to the remote data processingunit 1702.

The remote data processing unit 1702 uses the data 1708 usable to derivethe rendition of the data at the relatively low level of quality 1706 toderive the rendition of the data at the relatively low level of quality1706. This may for example involve the remote data processing unit 1702decrypting the received data 1708 usable to derive the rendition of thedata at the relatively low level of quality 1706.

The remote data processing unit 1702 uses the reconstruction data 1707and the rendition of the data at the relatively low level of quality1706 to reconstruct the rendition of the data at the medium level ofquality 1704. As such, the rendition of the data at the relatively lowlevel of quality 1706 is used as a baseline for reconstructing therendition of the data at the medium level of quality 1704.

The remote data processing unit 1702 uses the reconstruction data 1705and the rendition of the data at the medium level of quality 1704 toreconstruct the rendition of the data at the relatively high level ofquality 1703. As such the rendition of the data at the medium level ofquality 1704 is used as a baseline for reconstructing the rendition ofthe data at the relatively high level of quality 1703.

In some examples, the data processing device 1701 is configured toencrypt only the rendition of the data at the relatively low level ofquality 1706. Without having the decrypted version of the data at therelatively low level of quality 1706, it may not be possible toreconstruct the rendition of the data at the medium level of quality1704 and/or the relatively high level of quality 1703. Encrypting onlythe rendition of the data at the relatively low level of quality 1706,rather than also encrypting the rendition of the data at the relativelyhigh level of quality 1703 and/or the rendition of the data at themedium level of quality 1704 may reduce complexity and/or processingtime without significantly sacrificing data security requirements.

The reader is referred to WO-A2-2013/011496, which describes variousexamples of encoding techniques, which may be used in association withthe techniques described herein. The entire contents ofWO-A2-2013/011496 are hereby incorporated herein by reference.

Referring to FIG. 18, there is shown a schematic block diagram of anexample of a data processing system 1800.

The data processing system 1800 may be used to encode and decode data.The signal processing system 1800 includes a data processing device 1801and a remote data processing unit 1802. In this example, the dataprocessing device 1801 comprises encoder functionality. The encoderfunctionality may be provided by one or more hardware and/or one or moresoftware components. In this example, the remote data processing unit1802 comprises decoding functionality. The decoding functionality may beprovided by one or more hardware and/or one or more software components.

In this example, the data processing system 1800 is used to implement ahierarchical encoding technique, as will now be described.

In this example, the data processing device 1801 obtains first data1803. In this example, the first data 1803 is a first set of sampledvalues.

In this example, the data processing device 1801 obtains second data1804. In this example, the second data 1804 is a second set of sampledvalues.

In this example, the data processing device 1801 derives third data1805. In this example, the third data 1805 is a first set of dataelements. Data elements in the first set of data elements 1805 areindicative of a measure of a change between a sampled value in the firstset of sampled values 1803 and a corresponding sampled value in thesecond set of sampled values 1804. In this specific example, dataelements in the first set of data elements 1805 are indicative of adifference between a sampled value in the first set of sampled values1803 and a corresponding sampled value in the second set of sampledvalues 1804.

In this example, the data processing device 1801 obtains fourth data1806. In this example, the fourth data 1806 is a third set of sampledvalues.

In this example, the data processing device 1801 derives fifth data1807. In this example, the fifth data 1807 is a second set of dataelements. Data elements in the second set of data elements 1807 areindicative of a measure of a change between a sampled value in thesecond set of sampled values 1804 and a corresponding sampled value inthe third set of sampled values 1806. In this specific example, dataelements in the second set of data elements 1807 are indicative of adifference between a sampled value in the second set of sampled values1804 and a corresponding sampled value in the third set of sampledvalues 1806.

In this example, the first set of sampled values 1803, the first set ofdata elements 1805 and the second set of data elements 1807 aretransmitted to the remote data processing unit 1802. Some or all of thefirst set of sampled values 1803, the first set of data elements 1805and the second set of data elements 1807 may be processed prior totransmission to the remote data processing unit 1802. For example, someor all of the first set of sampled values 1803, the first set of dataelements 1805 and the second set of data elements 1807 may be encryptedand/or encoded.

In this example, the remote data processing unit 1802 obtains the firstset of sampled values 1803. The remote data processing unit 1802 mayprocess received data prior to obtaining the first set of sampled values1803.

In this example, the remote data processing unit 1802 obtains the firstset of data elements 1805. The remote data processing unit 1802 mayprocess received data prior to obtaining the first set of data elements1805. The remote data processing unit 1802 reconstructs the second setof sampled values 1804 using the first set of sampled values 1803 andthe first set of data elements 1805.

In this example, the remote data processing unit 1802 obtains the secondset of data elements 1807. The remote data processing unit 1802 mayprocess received data prior to obtaining the second set of data elements1807. The remote data processing unit 1802 reconstructs the third set ofsampled values 1806 using the reconstructed second set of sampled values1804 and the second set of data elements 1807.

The first set of sampled values 1803 may be considered to correspond toa base layer, similar to the rendition of data at a relatively low levelof quality 1704 described above. The first set of data elements 1805 maybe considered to correspond to enhancement data, similar to thereconstruction data 1707 described above. The second set of sampledvalues 1804 may be considered to correspond to an enhanced layer,similar to the rendition of data at a medium level of quality 1704described above. The second set of data elements 1807 may be consideredto correspond to enhancement data, similar to the reconstruction data1705 described above. The third set of sampled values 1806 may beconsidered to correspond to an enhanced layer, similar to the renditionof data at a relatively high level of quality 1703 described above. Assuch, a first snapshot of the sampled values may be seen as an initialbase layer and one or more further snapshots of the sampled values maybe seen as one or more enhancement layers. This allows the applicationof a hierarchical approach over the sequence of sets of sampled values.By applying such a hierarchical encoding technique in relation to thesampled values described above, a snapshot of the sampled values at agiven point in time may be considered to be a temporal enhancement of abase layer, corresponding to a set of measures of difference between thesnapshot and one or more previous snapshots.

Referring to FIG. 19, there is shown a schematic block diagram of anexample of an apparatus 1900. The apparatus 1900 may be a dataprocessing device as described above. The apparatus 1900 may be a remotedata processing unit as described above.

In this example, the apparatus 1900 comprises one or more processors1901 configured to process information and/or instructions. The one ormore processors 1901 may comprise a central processing unit (CPU). Theone or more processors 1901 are coupled with a bus 1902. Operationsperformed by the one or more processors 1901 may be carried out byhardware and/or software. The one or more processors 1901 may comprisemultiple co-located processors or multiple disparately locatedprocessors.

In this example, the apparatus 1900 comprises computer-useable volatilememory 1903 configured to store information and/or instructions for theone or more processors 1901. The computer-useable volatile memory 1903is coupled with the bus 1902. The computer-useable volatile memory 1903may comprise random access memory (RAM).

In this example, the apparatus 1900 comprises computer-useablenon-volatile memory 1904 configured to store information and/orinstructions for the one or more processors 1901. The computer-useablenon-volatile memory 1904 is coupled with the bus 1902. Thecomputer-useable non-volatile memory 1904 may comprise read-only memory(ROM).

In this example, the apparatus 1900 comprises one or more data-storageunits 1905 configured to store information and/or instructions. The oneor more data-storage units 1905 are coupled with the bus 1902. The oneor more data-storage units 1905 may for example comprise a magnetic oroptical disk and disk drive or a solid-state drive (SSD).

In this example, the apparatus 1900 comprises one or more input/output(I/O) devices 1906 configured to communicate information to and/or fromthe one or more processors 1901. The one or more I/O devices 1906 arecoupled with the bus 1902. The one or more I/O devices 1906 may compriseat least one network interface. The at least one network interface mayenable the apparatus 1900 to communicate via one or more datacommunications networks. The one or more I/O devices 1906 may enable auser to provide input to the apparatus 1900 via one or more inputdevices (not shown). The one or more input devices may include forexample a keyboard, a mouse, a joystick etc. The one or more I/O devices1906 may enable information to be provided to a user via one or moreoutput devices (not shown). The one or more output devices may forexample include a display screen.

Various other entities are depicted for the apparatus 1900. For example,when present, an operating system 1907, data processing module 1908, oneor more further modules 1909, and data 1910 are shown as residing inone, or a combination, of the computer-usable volatile memory 1903,computer-usable non-volatile memory 1904 and the one or moredata-storage units 1905. The data processing module 1908 may beimplemented by way of computer program code stored in memory locationswithin the computer-usable non-volatile memory 1904, computer-readablestorage media within the one or more data-storage units 1905 and/orother tangible computer-readable storage media. Examples of tangiblecomputer-readable storage media include, but are not limited to, anoptical medium (e.g., CD-ROM, DVD-ROM or Blu-ray), flash memory card,floppy or hard disk or any other medium capable of storing computerreadable instructions such as firmware or microcode in at least one ROMor RAM or Programmable ROM (PROM) chips or as an Application SpecificIntegrated Circuit (ASIC).

The apparatus 1900 may therefore comprise a data processing module 1908which can be executed by the one or more processors 1901. The dataprocessing module 1908 can be configured to include instructions toimplement at least some of the operations described herein. Duringoperation, the one or more processors 1901 launch, run, execute,interpret or otherwise perform the instructions in the data processingmodule 1908.

It will be appreciated that the apparatus 1900 may comprise more, fewerand/or different components from those depicted in FIG. 19.

The apparatus 1900 may be located in a single location or may bedistributed in multiple locations.

Although at least some aspects of the examples described herein withreference to the drawings comprise computer processes performed inprocessing systems or processors, examples described herein also extendto computer programs, for example computer programs on or in a carrier,adapted for putting the examples into practice. The carrier may be anyentity or device capable of carrying the program.

The techniques described herein may be implemented in software orhardware, or may be implemented using a combination of software andhardware. They may include configuring an apparatus 1900, such as forexample a data processing device and/or a remote data processing unit tocarry out and/or support any or all of techniques described herein.

Examples described above relate to compression and transmission oftelemetry data substantially in real-time. Examples described aboverelate to encoding telemetry data as a stream using with temporalencoding. In some examples, the telemetry data is encrypted. In someexample, the compression or encoding is lossless. In some examples, theapparatuses and method are compatible with existing hardware and/orsoftware systems.

Various measures (for example apparatuses, methods and computerprograms) are provided for processing telemetry data. Sampled data isobtained. The sampled data is output data from a plurality of sensorsassociated with a vehicle. First and second sets of sampled values aregenerated using the sampled data. The first set of sampled values isassociated with a first sampling time. The second set of sampled valuesis associated with a second, subsequent sampling time. A set of dataelements is derived. A data element is indicative of a measure of achange between a sampled value in the first set of sampled values and acorresponding sampled value in the second set of sampled values. The setof data elements is encoded. Data comprising at least the encoded set ofdata elements is output for transmission to a remote data processingunit.

The first set of sampled values, the second set of sampled values andthe set of data elements may be arranged as a same type of array.

The array may have two or more than two dimensions.

A position of a given sampled value in the first set and/or a givensampled value the second set and/or a given data element in the set ofdata elements may be determined based on at least one mapping rule.

The sensors in the plurality of sensors may be in a given order and themapping rule may be configured to preserve an order of the sampledvalues in the first and/or the sampled values in the second set and/orthe data elements in the set of data elements with respect to the givenorder of the sensors with which the sampled values in the first setand/or the sampled values in the second set and/or the data elements inthe set of data elements are associated.

An identity of a given sensor with which a given sampled value in thefirst set and/or a given sampled value in the second set and/or a givendata element in the set of data elements is associated may bedeterminable based solely on a position of the given sampled value inthe first set and/or the given sampled value in the second set and/orthe given data element in the set of data elements respectively.

The sensors in the plurality of sensors may be in a given order and themapping rule may be configured to allow an order of the sampled valuesin the first set and/or the sampled values in the second set and/or thedata elements in the set of data elements to be different with respectto the given order of the sensors with which the sampled values in thefirst and/or the sampled values in the second set and/or the dataelements in the set of data elements are associated.

An identity of a given sensor with which a given sampled value in thefirst set and/or a given sampled value in the second set and/or a givendata element in the set of data elements is associated may beindeterminable based solely on a position of the given sampled value inthe first set and/or the given sampled value in the second set and/orthe given data element in the set of data elements respectively.

Correlation data may be output for transmission to the remote dataprocessing unit. The correlation data may be arranged to associate thegiven sampled value in the first set and/or the given sampled value inthe second set and/or the given data element in the set of data elementswith the given sensor with which the given sampled value in the firstset and/or the given sampled value in the second set and/or the givendata element in the set of data elements is associated.

A position may be determined for some or all of the sampled values inthe first set and/or for some or all of the sampled values in the secondset and/or for some or all of the data elements in the set of dataelements based on a measure of a variation of the output data from theplurality of sensors.

The sampled values and/or the data elements associated with sensorswhose output data changes relatively frequently, compared to sensorswhose output data changes relatively infrequently, may be groupedtogether.

The sampled values and/or data elements associated with sensors whoseoutput data changes relatively infrequently, compared to sensors whoseoutput data changes relatively frequently, may be grouped together.

A sampled value obtained from a given sensor in the plurality of sensorsat the first sampling time and a sampled value obtained from the givensensor at the second sampling time may be mapped to a given position inthe first set and to the same given position in the second set.

A sampled value obtained from a given sensor in the plurality of sensorsat the first sampling time and a sampled value obtained from the givensensor at the second sampling time may be mapped to a first position inthe first set and a second, different position in the second set.

The output data from at least one first sensor in the plurality ofsensors may be sampled at a first sampling rate. The output data from atleast one second sensor in the plurality of sensors may be sampled at asecond, higher sampling rate. The first set of sampled values and secondset of sampled values may be generated at the second, higher samplingrate.

The second set of sampled values may be generated using a sampled valueobtained using the at least one first sensor at the first sampling time.

The second set of sampled values may be generated using a sampled valueobtained using the at least one second sensor at the second samplingtime.

Data based on the first set of sampled values may be output fortransmission to the remote data processing unit.

The first set of sampled values may be encoded. Data based on theencoded first set of sampled values may be output for transmission tothe remote data processing unit.

The set of data elements may be encoded using a first codec. The firstset of sampled values may be encoded using a second, different codec.

The first set of sampled values and/or the set of data elements may beencoded based on at least one characteristic of the first set of sampledvalues and/or the set of data elements.

The first set of sampled values and/or the set of data elements may beencoded using an image and/or video encoding technique.

The first set of sampled values and/or the set of data elements may beencoded using a lossless encoding technique.

The first set of sampled values and/or the set of data elements may beencoded using an entropy encoding technique.

The first set of sampled values and/or the set of data elements may beencoded based on at least one characteristic of a communications channelbetween the data processing device and the remote data processing unit.

The at least one characteristic of the communications channel maycomprise a capacity of the communications channel.

The first set of sampled values and/or data derived from the first setof sampled values may be encrypted. The encrypted first set of sampledvalues and/or data derived from the first set of sampled values may beoutput for transmission to the remote data processing unit.

A data element in the set of data elements may be indicative of ameasure of a change between an unencrypted sampled value in the firstset of sampled values and a corresponding sampled value in the secondset of sampled values.

The data derived from the first set of sampled values may be a renditionof the first set of sampled values at a lower level of quality than alevel of quality of the first set of sampled values.

Some or all of the sampled values in the first set and/or the second setmay be unquantized values of output data.

At least one further set of sampled values may be generated using thesampled output data. The at least one further set of sampled values isassociated with at least one further sampling time. At least one furtherset of data elements is derived. The at least one further set of dataelements is encoded. The at least one further encoded set of dataelements is output for transmission to the remote data processing unit.

A data element in the at least one further set of data elements may beindicative of a measure of a change between a sampled value in the atleast one further set and a sampled value in one of the first set, thesecond set or another set of data elements.

The transmission may comprise wireless transmission to the remote dataprocessing unit.

The transmission may comprise transmission via one or more satellitecommunications channels.

The transmission may occur substantially in real-time.

The vehicle may be an aircraft.

The transmission may occur while the aircraft is in-flight.

The sampled data may be obtained from one or more flight-dataacquisition units, FDAUs.

The sampled data may be obtained by sampling the output data of theplurality of sensors.

The second set of sampled values may not be encoded.

The second set of sampled values may not be output for transmission tothe remote data processing unit.

Various measures (for example apparatuses, methods and computerprograms) are provided for processing telemetry data. Data comprising anencoded set of data elements is received from a remote data processingdevice. A data element in the encoded set of data elements is indicativeof a measure of a change between a sampled value in a first set ofsampled values and a corresponding sampled value in a second set ofsampled values. The first set of sampled values is associated with afirst sampling time and the second set of sampled values is associatedwith a second, subsequent sampling time. The first and second sets ofsampled values have been generated using sampled data. The sampled datais based on output data sampled from a plurality of sensors associatedwith a vehicle. The encoded set of data elements is decoded. At leastthe decoded set of data elements is used to recover the second set ofsampled values.

The first set of sampled values, the second set of sampled values andthe set of data elements may all be arranged as a same type of array.

The array may have two or more than two dimensions.

An identity of a given sensor with which a given sampled value in thefirst set and/or a given sampled value in the second set and/or a givendata element in the set of data elements is associated may be determinedbased solely on determining of a position of the given sampled value inthe first set and/or the given sampled value in the second set and/orthe given data element in the set of data elements respectively.

Correlation data may be received from the data processing device. Thecorrelation data may be arranged to associate a given sampled value inthe first set and/or the given sampled value in the second set and/orthe given data element in the set of data elements with a given sensorwith which the given sampled value in the first set and/or the givensampled value in the second set and/or the given data element in the setof data elements is associated. An identity of the given sensor may bedetermined based on the received correlation data.

Data based on the first set of sampled values may be received. The databased on the first set of sampled values may be used to recover thesecond set of data elements.

The data based on the first set of sampled values may be received in anencrypted form. The data based on the first set of sampled values may bedecrypted.

The data based on the first set of sampled values may be received in anencoded form. The data based on the first set of sampled values may bedecoded.

The encoded set of data elements may be decoded using a first codec. Thedata based on the first set of sampled values may be decoded using asecond, different codec.

Some or all of the sampled values in the first set and/or the second setmay be unquantized values.

At least one further encoded set of data elements may be received fromthe remote data processing device. The at least one further encoded setof data elements is decoded. At least the decoded at least one furtherset of data elements is used to recover at least one further set ofsampled values.

A data element in the at least one further set of data elements isindicative of a measure of a change between a sampled value in the atleast one further set and a sampled value in one of the first set, thesecond set or another set of data elements.

The data comprising the encoded set of data elements may be receivedwirelessly.

The data comprising the encoded set of data elements may be received viaone or more satellite communications channels.

The vehicle may be an aircraft.

The data comprising the encoded set of data elements may be receivedwhile the aircraft is in-flight.

Various measures (for example apparatuses, methods and computerprograms) are provided for processing telemetry data in which outputdata from at least one sensor associated with a vehicle is sampled.First and second sets of sampled data are generated using the sampledoutput data. The first set of sampled data is associated with a firstsampling time. The second set of sampled data is associated with asecond sampling time. At least one set of data elements is derived. Adata element is indicative of a measure of a change between sampled datain the first set and corresponding sampled data in the second set. Atleast the set of data elements is encoded. The encoded set of dataelements is output for transmission to a data processing unit.

Various measures (for example apparatuses, methods and computerprograms) are provided for processing meteorological data in whichoutput data from at least one sensors associated with an aircraft issampled while the aircraft is in-flight. The at least one sensor isassociated with at least one meteorological property. At least one setof sampled data is generated using the sampled output data while theaircraft is in-flight. The at least one set of sampled data isassociated with at least one sampling time. The at least one set ofsampled data and/or data derived from the at least one set of sampleddata is encoded while the aircraft is in-flight. The encoded data isoutput for transmission to a remote data processing unit while theaircraft is in-flight.

The above embodiments are to be understood as illustrative examples.Further embodiments are envisaged.

In some examples, the data processing device is configured to receivemultimedia data. The multimedia may comprise audio, image and/or videodata. The data processing device may be configured to output themultimedia data or data based on the multimedia data for transmission tothe remote data processing unit. In some examples, the data processingdevice is configured to process the multimedia data prior to outputtingthe multimedia data for transmission to the remote data processing unit.Examples of such processing include, but are not limited to, encodingand compressing the multimedia data. The multimedia data or data basedon the multimedia data may be output for transmission to the remote dataprocessing unit together with the telemetry data described above orseparate from such telemetry data.

In examples described above, the data processing device is configured tooutput at least the encoded set of data elements for transmission to theremote data processing unit. In some examples, the data processingdevice is configured to delete data output for transmission to theremote data processing unit following output of the data. In suchexamples, the amount of memory required to store telemetry data, forexample during a journey, may be reduced. In other examples, the dataprocessing device is configured to store some or all of the data outputfor transmission to the remote data processing unit following output ofthe data. In such examples, the stored data may subsequently beretrieved and analyzed, for example following a journey involving thevehicle.

It is to be understood that any feature described in relation to any oneembodiment may be used alone, or in combination with other featuresdescribed, and may also be used in combination with one or more featuresof any other of the embodiments, or any combination of any other of theembodiments. Furthermore, equivalents and modifications not describedabove may also be employed without departing from the scope of theinvention, which is defined in the accompanying claims.

What is claimed is:
 1. A data processing unit for processing telemetrydata, the data processing unit being configured to: receive datacomprising an encoded set of data elements from a remote data processingdevice, a data element in the encoded set of data elements beingindicative of a measure of a change between a sampled value in a firstset of sampled values and a corresponding sampled value in a second setof sampled values, a position of a given data element in the set of dataelements having been determined based on at least one dynamic mappingrule, the first set of sampled values being associated with a firstsampling time and the second set of sampled values being associated witha second, subsequent sampling time, the first and second sets of sampledvalues having been generated using sampled data, the sampled data beingbased on output data sampled from at least one first sensor of aplurality of sensors associated with a vehicle and output data from atleast one second sensor of the plurality of sensors, the output datafrom the at least first sensor having been sampled at a first samplingrate and the output data from the at least one second sensor having beensampled at a second, higher sampling rate, wherein a position of a givensample in the first set and a position of a given sample in the secondset are based on the at least one dynamic mapping rule; decode theencoded set of data elements; and use at least the decoded set of dataelements to recover the second set of sampled values.
 2. A dataprocessing unit according to claim 1, the data processing unit beingconfigured to: receive data based on the first set of sampled values;and use the data based on the first set of sampled values to recover thesecond set of data elements.
 3. A data processing unit according toclaim 2, wherein the data based on the first set of sampled values isreceived in an encrypted form, the data processing unit being configuredto decrypt the data based on the first set of sampled values.
 4. A dataprocessing unit according to claim 2, wherein the data based on thefirst set of sampled values is received in an encoded form, the dataprocessing unit being configured to decode the data based on the firstset of sampled values.
 5. A data processing unit according to claim 4,the data processing unit being configured to decode the encoded set ofdata elements using a first codec and to decode the data based on thefirst set of sampled values using a second, different codec.
 6. A dataprocessing unit according to claim 1, wherein the first set of sampledvalues, the second set of sampled values and the set of data elementsare all arranged as a same type of array.
 7. A data processing unitaccording to claim 6, wherein the array has two or more than twodimensions.
 8. A data processing unit according to claim 1, the dataprocessing unit being configured to determine an identity of a givensensor with which a given sampled value in the first set and/or a givensampled value in the second set and/or a given data element in the setof data elements is associated based solely on determining of a positionof the given sampled value in the first set and/or the given sampledvalue in the second set and/or the given data element in the set of dataelements respectively.
 9. A data processing unit according to claim 1,the data processing unit being configured to: receive correlation datafrom the remote data processing device, the correlation data beingarranged to associate a given sampled value in the first set and/or thegiven sampled value in the second set and/or the given data element inthe set of data elements with a given sensor with which the givensampled value in the first set and/or the given sampled value in thesecond set and/or the given data element in the set of data elements isassociated; and determine an identity of the given sensor based on thereceived correlation data.
 10. A data processing unit according to claim1, wherein some or all of the sampled values in the first set and/or thesecond set are unquantised values.
 11. A data processing unit accordingto claim 1, the data processing device being configured to: receive atleast one further encoded set of data elements from the remote dataprocessing device; decode the at least one further encoded set of dataelements; and use at least the decoded at least one further set of dataelements to recover at least one further set of sampled values.
 12. Adata processing unit according to claim 11, wherein a data element inthe at least one further set of data elements is indicative of a measureof a change between a sampled value in the at least one further set anda sampled value in one of the first set, the second set or another setof data elements.
 13. A data processing unit according to claim 1, thedata processing unit being configured to receive the data comprising theencoded set of data elements wirelessly.
 14. A method of processingtelemetry data, the method comprising, at a data processing unit:receiving data comprising an encoded set of data elements from a remotedata processing device, a data element in the encoded set of dataelements being indicative of a measure of a change between a sampledvalue in a first set of sampled values and a corresponding sampled valuein a second set of sampled values, a position of a given data element inthe set of data elements having been determined based on at least onedynamic mapping rule, the first set of sampled values being associatedwith a first sampling time and the second set of sampled values beingassociated with a second, subsequent sampling time, the first and secondsets of sampled values having been generated using sampled data, thesampled data being based on output data sampled from at least one firstsensor of a plurality of sensors associated with a vehicle and outputdata from at least one second sensor of the plurality of sensors, theoutput data from the at least first sensor having been sampled at afirst sampling rate and the output data from the at least one secondsensor having been sampled at a second, higher sampling rate, wherein aposition of a given sample in the first set and a position of a givensample in the second set are based on the at least one dynamic mappingrule; decoding the encoded set of data elements; and using at least thedecoded set of data elements to recover the second set of sampledvalues.
 15. A method according to claim 14, the method comprising:receiving data based on the first set of sampled values; and using thedata based on the first set of sampled values to recover the second setof data elements.
 16. A method according to claim 15, wherein the databased on the first set of sampled values is received in an encryptedform, the data processing unit being configured to decrypt the databased on the first set of sampled values.
 17. A method according toclaim 15, wherein the data based on the first set of sampled values isreceived in an encoded form, the data processing unit being configuredto decode the data based on the first set of sampled values, whereinsaid decoding of the encoded set of data elements comprises using afirst codec, and wherein said decoding of the data based on the firstset of sampled values comprises using a second, different codec.
 18. Amethod according to claim 14, wherein the first set of sampled values,the second set of sampled values and the set of data elements are allarranged as a same type of array, wherein the array has two or more thantwo dimensions.
 19. A method according to claim 14, comprising:receiving correlation data from the remote data processing device, thecorrelation data being arranged to associate a given sampled value inthe first set and/or the given sampled value in the second set and/orthe given data element in the set of data elements with a given sensorwith which the given sampled value in the first set and/or the givensampled value in the second set and/or the given data element in the setof data elements is associated; and determining an identity of the givensensor based on the received correlation data.
 20. A method according toclaim 14, comprising: receiving at least one further encoded set of dataelements from the remote data processing device; decoding the at leastone further encoded set of data elements; and using at least the decodedat least one further set of data elements to recover at least onefurther set of sampled values, wherein a data element in the at leastone further set of data elements is indicative of a measure of a changebetween a sampled value in the at least one further set and a sampledvalue in one of the first set, the second set or another set of dataelements.